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awesome-deep-time-series-representations
A curated list of state-of-the-art papers on deep learning for universal representations of time series.
https://github.com/itouchz/awesome-deep-time-series-representations
Last synced: about 3 hours ago
JSON representation
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Related Surveys (Latest Update: October, 2023)
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Time-Series Data Mining and Analysis
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- A survey of methods for time series change point detection
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling
- Time-series clustering – A decade review
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- Approaches and Applications of Early Classification of Time Series: A Review
- An empirical survey of data augmentation for time series classification with neural networks
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- An Experimental Review on Deep Learning Architectures for Time Series Forecasting
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Long sequence time-series forecasting with deep learning: A survey
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
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Representation Learning
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications
- Representation learning for knowledge fusion and reasoning in Cyber–Physical–Social Systems: Survey and perspectives
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
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Related Surveys (Latest Update: May, 2024)
-
Time-Series Data Mining and Analysis
- Deep learning for time series classification: a review
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- A survey of methods for time series change point detection
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
-
Representation Learning
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
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Research Papers (Latest Update: ICLR 2024)
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Learning-Focused Approaches
- - 3-030-46133-1_19) | ECML PKDD |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - scale self-supervised representation learning with temporal alignment for multi-rate time series modeling](https://www.sciencedirect.com/science/article/pii/S0031320323006416) | Pattern Recognition |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
-
Neural Architectural Approaches
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
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Related Surveys (Latest Update: April, 2024)
-
Time-Series Data Mining and Analysis
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- A survey of methods for time series change point detection
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
-
Representation Learning
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
-
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Related Surveys (Latest Update: June, 2024)
-
Time-Series Data Mining and Analysis
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Deep learning for time series classification: a review
- A survey of methods for time series change point detection
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Causal inference for time series analysis: problems, methods and evaluation
- Foundation Models for Time Series Analysis: A Tutorial and Survey
- Large Language Models for Time Series: A Survey
- Empowering Time Series Analysis with Large Language Models: A Survey
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Deep learning for time series classification: a review
- A survey of methods for time series change point detection
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
-
Representation Learning
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
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Related Surveys (Latest Update: July, 2024)
-
Time-Series Data Mining and Analysis
- Empowering Time Series Analysis with Large Language Models: A Survey
- Time-series forecasting with deep learning: a survey
- Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
- Time Series Data Augmentation for Deep Learning: A Survey
- Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
- Label-efficient Time Series Representation Learning: A Review
- Diffusion Models for Time Series Applications: A Survey
- A Survey on Time-Series Pre-Trained Models
- Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review
- Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms
- Unsupervised Representation Learning for Time Series: A Review
- A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- Approaches and Applications of Early Classification of Time Series: A Review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Large Language Models for Time Series: A Survey
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- A survey of methods for time series change point detection
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Deep Learning for Time Series Anomaly Detection: A Survey
- Transformers in Time Series: A Survey
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Foundation Models for Time Series Analysis: A Tutorial and Survey
- Deep Time Series Models: A Comprehensive Survey and Benchmark
- Survey on time series motif discovery - Lippe University of Applied Sciences | WIDM | 2017 |
- Deep learning for time series classification: a review
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review on Outlier/Anomaly Detection in Time Series Data
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Discrete Wavelet Transform-based Time Series Analysis and Mining
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- A survey of methods for time series change point detection
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Deep learning for time series classification: a review
- A survey of methods for time series change point detection
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- An Experimental Review on Deep Learning Architectures for Time Series Forecasting
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Deep Learning for Time-Series Analysis
- Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
- Anomaly Detection for IoT Time-Series Data: A Survey - J | 2019 |
- A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
- A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series - RSA | 2020 |
- Neural Time Series Analysis with Fourier Transform: A Survey
- Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Data Augmentation techniques in time series domain: a survey and taxonomy
- Time-Series Data Mining
- A Review of Deep Learning Models for Time Series Prediction
- Survey and Evaluation of Causal Discovery Methods for Time Series
- A Review of Recurrent Neural Network-Based Methods in Computational Physiology
- A Survey on Dimensionality Reduction Techniques for Time-Series Data
- Position: What Can Large Language Models Tell Us about Time Series Analysis
-
Representation Learning
- Representation Learning: A Review and New Perspectives
- A Survey of Multi-View Representation Learning
- A Survey on Representation Learning for User Modeling
- Contrastive Representation Learning: A Framework and Review
- Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data
- Self-Supervised Representation Learning: Introduction, advances, and challenges
- Evaluation Methods for Representation Learning: A Survey
- A Comprehensive Survey on Deep Graph Representation Learning
- A survey on deep geometry learning: From a representation perspective
- Deep Multimodal Representation Learning: A Survey
- A survey on deep geometry learning: From a representation perspective
- A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- Graph Representation Learning Meets Computer Vision: A Survey
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- Dynamic Graph Representation Learning with Neural Networks: A Survey
- Multiscale Representation Learning for Image Classification: A Survey
- A Survey on Protein Representation Learning: Retrospect and Prospect
- A survey on deep geometry learning: From a representation perspective
- Survey of Deep Representation Learning for Speech Emotion Recognition
- Graph Representation Learning and Its Applications: A Survey
- Self-Supervised Speech Representation Learning: A Review
-
-
Research Papers (Latest Update: ICML 2024)
-
Learning-Focused Approaches
- - 3-030-46133-1_19) | ECML PKDD |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-46133-1_19) | ECML PKDD |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
-
Neural Architectural Approaches
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
-
-
Research Papers (Latest Update: NeurIPS 2023)
-
Neural Architectural Approaches
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
-
Learning-Focused Approaches
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
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- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
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- - 3-030-46133-1_19) | ECML PKDD |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-46133-1_19) | ECML PKDD |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
-
Data-Centric Approaches
- - task representation learning method for time series classification and retrieval](https://www.sciencedirect.com/science/article/pii/S0020025520312287) | Information Sciences |
- - series clustering and classification using visual perception](https://www.sciencedirect.com/science/article/pii/S0950705120306808) | KBS |
-
-
Research Papers (Latest Update: KDD 2024)
-
Learning-Focused Approaches
- - Series Embedding](https://proceedings.mlr.press/v84/wu18b.html) | AISTATS |
- - Visual Spatial Alignment](https://proceedings.neurips.cc/paper/2020/hash/328e5d4c166bb340b314d457a208dc83-Abstract.html) | NeurIPS |
- - Temporal Pretext Learning for Self-Supervised Video Representation](https://ojs.aaai.org/index.php/AAAI/article/view/20248) | AAAI |
- - Training for Irregular Multivariate Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/25876) | AAAI |
- - Series Sensing Signals in Factorized Orthogonal Latent Space](https://openreview.net/forum?id=l4CZCKXoSn) | NeurIPS |
- - 3-030-46133-1_19) | ECML PKDD |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - to-Sequence Autoencoding for Unsupervised Learning of Audio Segmentation and Representation](https://ieeexplore.ieee.org/document/8736337) | IEEE/ACM TASLP |
- - centric Audio-visual Representation Learning](https://proceedings.neurips.cc/paper_files/paper/2021/hash/51200d29d1fc15f5a71c1dab4bb54f7c-Abstract.html) | NeurIPS |
- - Series](https://openreview.net/forum?id=sOQBHlCmzp) | NeurIPS |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - supervised Video Representation Learning by Uncovering Spatio-temporal Statistics](https://ieeexplore.ieee.org/abstract/document/9352025) | IEEE TPAMI |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask
- - 3-030-10928-8_34) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - 3-030-46133-1_19) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - temporal Representation](https://dl.acm.org/doi/abs/10.1145/3503161.3547783) | MM |
- - to-End Incomplete Time-Series Modeling From Linear Memory of Latent Variables](https://ieeexplore.ieee.org/document/8685795) | IEEE TCYB |
- - Rep: Self-supervised time series representation learning from robot sensor data](https://sslneurips22.github.io/paper_pdfs/paper_74.pdf) | NeurIPS (Workshop) |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - 3-030-46133-1_19) | ECML PKDD |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - Supervised Video Representation Learning](https://ieeexplore.ieee.org/abstract/document/9713748) | IEEE TIP |
- - 3-030-46133-1_19) | ECML PKDD |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - based Framework for Unsupervised Multivariate Time Series Representation Learning](https://www.vldb.org/pvldb/vol17/p386-wang.pdf) | VLDB |
- - Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://arxiv.org/abs/2309.05305) | AAAI |
- - REP: REPRESENTATION LEARNING FOR TIME SERIES USING TIME-EMBEDDINGS](https://openreview.net/forum?id=3y2TfP966N) | ICLR |
- - Contrast for Self-Supervised Video Representation Learning](https://proceedings.neurips.cc/paper/2020/hash/5c9452254bccd24b8ad0bb1ab4408ad1-Abstract.html) | NeurIPS |
- - Consistency](https://openaccess.thecvf.com/content/CVPR2021/html/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.html) | CVPR |
- - 3-030-46133-1_19) | ECML PKDD |
- - aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
- - 3-030-10928-8_34) | ECML PKDD |
- - supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
- - Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
- - Task Time Series Model](https://arxiv.org/abs/2403.00131) | arXiv |
- - Equivariant Contrastive Video Representation Learning](https://openaccess.thecvf.com/content/ICCV2021/html/Jenni_Time-Equivariant_Contrastive_Video_Representation_Learning_ICCV_2021_paper.html) | ICCV |
- - Series Representation Learning via Temporal and Contextual Contrasting](https://www.ijcai.org/proceedings/2021/324) | IJCAI |
- - Supervised Time Series Representation Learning with Temporal-Instance Similarity Distillation](https://openreview.net/forum?id=nhtkdCvVLIh) | ICML (Workshop) |
- - Architecture Self-supervised Video Representation Learning](https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Cross-Architecture_Self-Supervised_Video_Representation_Learning_CVPR_2022_paper.html) | CVPR |
- - Supervised Video Representation Learning with Hierarchical Consistency](https://openaccess.thecvf.com/content/CVPR2022/html/Qing_Learning_From_Untrimmed_Videos_Self-Supervised_Video_Representation_Learning_With_Hierarchical_CVPR_2022_paper.html) | CVPR |
- - supervised Video Representation Learning via Ranking-based Transformation Recognition](https://openaccess.thecvf.com/content/CVPR2022/html/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.html) | CVPR |
- - Supervised Video Representation Learning](https://bmvc2022.mpi-inf.mpg.de/0541.pdf) | BMVC |
- - Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity](https://ojs.aaai.org/index.php/AAAI/article/view/20047) | AAAI |
- - Training Framework for Masked Time-Series Modeling](https://openreview.net/forum?id=ginTcBUnL8) | NeurIPS |
- - MAE: Self-Supervised Masked Time Series Autoencoders](https://arxiv.org/abs/2301.08871) | arXiv |
- - Grained Video Representation Learning](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.html) | CVPR |
- - BASED RECONSTRUCTION FOR TIME-SERIES CONTRASTIVE LEARNING](https://openreview.net/forum?id=3zQo5oUvia) | ICLR |
- - Patch Prediction: Adapting Language Models for Time Series Representation Learning](https://openreview.net/forum?id=Rx9GMufByc) | ICML |
- - Training Framework for Siamese Time-Series Modeling](https://openreview.net/forum?id=wrTzLoqbCg) | ICML |
- - Task Self-Supervised Time-Series Representation Learning](https://arxiv.org/abs/2303.01034) | arXiv |
- - training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis](https://openreview.net/pdf?id=aR3uxWlZhX) | ICML |
-
Neural Architectural Approaches
- - Sampled Time Series](https://papers.nips.cc/paper_files/paper/2019/hash/42a6845a557bef704ad8ac9cb4461d43-Abstract.html) | NeurIPS |
- - time action representation with temporal encoding and deep compression](https://ieeexplore.ieee.org/iel7/76/4358651/09051710.pdf) | IEEE TCSVT |
- - series Classification and Forecasting](https://ieeexplore.ieee.org/abstract/document/9679144) | ICDM |
- - ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data](https://www.ijcai.org/proceedings/2021/414) | IJCAI |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Evolving Graphs via Spatial-Temporal Graph Attention Networks](https://dl.acm.org/doi/10.1145/3357384.3358155) | CIKM |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - MAE: A Generic Pre-trained Model for Multivariate Time Series with Missing Values](https://dl.acm.org/doi/abs/10.1145/3583780.3615097) | CIKM |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Dependent Heterogeneous Tabular Data](https://arxiv.org/abs/2302.06375) | arXiv |
- - Variation Modeling for General Time Series Analysis](https://openreview.net/forum?id=ju_Uqw384Oq) | ICLR |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Time Attention Networks for Irregularly Sampled Time Series](https://openreview.net/forum?id=4c0J6lwQ4_) | ICLR |
- - Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
- - Attention Model for Multivariate Time-Series Analysis](https://dl.acm.org/doi/abs/10.1145/3511808.3557386) | CIKM |
- - Attention Network for Video Representation Learning](https://openaccess.thecvf.com/content/CVPR2021/html/Guo_SSAN_Separable_Self-Attention_Network_for_Video_Representation_Learning_CVPR_2021_paper.html) | CVPR |
- - observed Time-series](https://arxiv.org/abs/2212.03560) | arXiv |
- - Time Transformer for Irregular Time Series Modeling](https://openreview.net/forum?id=YJDz4F2AZu) | NeurIPS |
- - Discrete State Space Models for Irregularly-Sampled Time Series](https://proceedings.mlr.press/v202/ansari23a.html) | ICML |
- - supervised Representation Learning](https://arxiv.org/abs/2303.13804) | arXiv |
- - DTW Hybrid Attention Network for Heterogeneous Time Series Analysis](https://dl.acm.org/doi/10.1145/3580305.3599549) | KDD |
- - SCALED EMBEDDING FOR LARGE-SCALE TIME SERIES PRETRAINING](https://openreview.net/forum?id=QVVSb0GMXK) | arXiv |
- - AWARE EMBEDDINGS](https://openreview.net/pdf?id=c56TWtYp0W) | ICLR |
-
Data-Centric Approaches
- - Sampled Time Series Modeling with Spline Networks](https://arxiv.org/abs/2210.10630) | ICML (Workshop) |
- - View Integrative Attention-Based Deep Representation Learning for Irregular Clinical Time-Series Data](https://ieeexplore.ieee.org/document/9769928) | IEEE JBHI |
- - Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion](https://proceedings.mlr.press/v162/yang22e.html) | ICML |
- - wise Action Representations for Long Videos via Sequence Contrastive Learning](https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.html) | CVPR |
- - supervised Contrastive Learning for Universal Time Series Representation Learning](https://arxiv.org/abs/2312.15709) | AAAI |
- - Abstract.html) | NeurIPS |
- - Modal Mutual Learning for Audio-Visual Speech Recognition and Manipulation](https://ojs.aaai.org/index.php/AAAI/article/view/20210) | AAAI |
- - Aware Augmentations](https://ojs.aaai.org/index.php/AAAI/article/view/25575) | AAAI |
- - Based Approach](https://arxiv.org/abs/2306.06579) | arXiv |
- - Variate Time-Series Modeling](https://ojs.aaai.org/index.php/AAAI/article/view/25915) | AAAI |
- - Scale Decomposition MLP-Mixer for Time Series Analysis](https://arxiv.org/abs/2310.11959) | arXiv |
- - series Foundation Models](https://openreview.net/forum?id=FVvf69a5rx) | ICML |
- - l24s) | ICLR |
- - Decayed Line Graphs](https://openreview.net/forum?id=Qamz7Q_Ta1k) | ICLR |
- - trained Transformers Are Large Time Series Models](https://openreview.net/forum?id=bYRYb7DMNo) | ICML |
- - CLR: Multi-Frequency Contrastive Learning Representation for Time Series](https://openreview.net/forum?id=ecO7WOIlMD) | ICML |
- - Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://openreview.net/forum?id=OJ4mMfGKLN) | NeurIPS |
- - Temporal Contrast for Self-supervised Video Representation Learning](https://openaccess.thecvf.com/content/WACV2022/html/Zhang_Hierarchically_Decoupled_Spatial-Temporal_Contrast_for_Self-Supervised_Video_Representation_Learning_WACV_2022_paper.html) | WACV |
- - supervised contrastive learning framework for univariate time series representation](https://www.sciencedirect.com/science/article/abs/pii/S0950705122002726) | KBS |
-
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