Sampling as a step of the publication

• DSIR
• Awesome Data Efficient LLM
• Small-Text
• Energizer - Learning framework for PyTorch based on PyTorch-Lightning.
• BRIEF - level optimization framework for efficient coreset selection in Large Language Model instruction tuning.
• Astartes

• Voxel51 ZCore
• Zero-Shot Coreset Selection: Efficient Pruning for Unlabeled Data
• FAIR SSL Data Curation
• Automatic data curation for self-supervised learning: A clustering-based approach

Sampling as a step of the publication

• DINOv2: Learning Robust Visual Features without Supervision
• DINOv3
• ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification
• Data selection for language models via importance resampling
• A survey on data selection for language models
• A Survey on Efficient Large Language Model Training: From Data-centric Perspectives
• Lead: Iterative data selection for efficient llm instruction tuning
• Greats: Online selection of high-quality data for llm training in every iteration
• Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration
• Freeal: Towards human-free active learning in the era of large language models
• Coresets over multiple tables for feature-rich and data-efficient machine learning
• Core-sets for fair and diverse data summarization
• A survey on data selection for llm instruction tuning
• Unleashing the power of data tsunami: A comprehensive survey on data assessment and selection for instruction tuning of language models
• GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems
• Data whisperer: Efficient data selection for task-specific llm fine-tuning via few-shot in-context learning
• In2core: Leveraging influence functions for coreset selection in instruction finetuning of large language models
• Staff: Speculative coreset selection for task-specific fine-tuning
• Self-evolved diverse data sampling for efficient instruction tuning
• Emergent properties with repeated examples
• Data selection in neural networks
• Not all samples are created equal: Deep learning with importance sampling
• Learning to select data for transfer learning with bayesian optimization
• Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics
• Data augmentation for regression machine learning problems in high dimensions
• Smart sampling and incremental function learning for very large high dimensional data
• Loss-proportional subsampling for subsequent erm
• Smart Query Sampling with Feature Coverage and Unsupervised Machine Learning
• Retrieve: Coreset selection for efficient and robust semi-supervised learning
• Efficient coreset selection with cluster-based methods
• Google Active Learning
• Decile CORDS - efficient training of deep learning models.
• rmunro/pytorch_active_learning
• ej0cl6/deep-active-learning
• ModAL
• libact - based active learning.
• AL Toolbox
• ALaaS
• AlpacaTag - based crowd annotation framework for sequence tagging
• Cure lab deep active learning
• DeepCore
• MiniCore - metric dissimilarity measures
• DataSplitters

Sampling as main topic of the publication

• Active learning for convolutional neural networks: A core-set approach
• Deep batch active learning by diverse, uncertain gradient lower bounds
• Variational adversarial active learning
• Selection via proxy: Efficient data selection for deep learning
• Moderate coreset: A universal method of data selection for real-world data-efficient deep learning
• Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds
• Single-Pass Object-Focused Data Selection
• Refined coreset selection: Towards minimal coreset size under model performance constraints
• Adversarial coreset selection for efficient robust training
• Efficient adversarial contrastive learning via robustness-aware coreset selection
• Coresets for data-efficient training of machine learning models
• Active deep probabilistic subsampling
• Deep probabilistic subsampling for task-adaptive compressed sensing
• Pruning-based Data Selection and Network Fusion for Efficient Deep Learning
• Grad-match: Gradient matching based data subset selection for efficient deep model training
• Automata: Gradient based data subset selection for compute-efficient hyper-parameter tuning
• Optimizing data collection for machine learning
• Making better use of unlabelled data in bayesian active learning
• Discriminative active learning
• Streaming active learning with deep neural networks
• Plug and play active learning for object detection
• Reinforced active learning for image segmentation
• Coreset sampling from open-set for fine-grained self-supervised learning
• Towards sustainable learning: Coresets for data-efficient deep learning
• D2 pruning: Message passing for balancing diversity and difficulty in data pruning
• Elfs: Label-free coreset selection with proxy training dynamics
• Coverage-centric coreset selection for high pruning rates
• Evolution-aware variance (EVA) coreset selection for medical image classification
• GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling
• Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology

Uncategorized

• A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances
• SupeRuier/awesome-active-learning
• baifanxxx/awesome-active-learning
• yongjin-shin/awesome-active-learning
• Clearloveyuan/awesome-active-learning-New
• PatrickZH/Awesome-Coreset-Selection
• gszfwsb/Awesome-Dataset-Reduction
• Alipy
• Baal
• Adaptive
• scikit-activeml - learn.
• pyrelational
• Coreax
• Scikit-Learn