https://github.com/xatta-trone/awesome-tdl
A curated collection of TDL (Tabular Deep Learning) resources—libraries, projects, tutorials, papers, and more—for researchers and developers in the field.
https://github.com/xatta-trone/awesome-tdl
List: awesome-tdl
tabular-data tabular-deep-learning
Last synced: 4 months ago
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A curated collection of TDL (Tabular Deep Learning) resources—libraries, projects, tutorials, papers, and more—for researchers and developers in the field.
- Host: GitHub
- URL: https://github.com/xatta-trone/awesome-tdl
- Owner: Xatta-Trone
- Created: 2025-02-17T05:12:06.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-25T04:54:49.000Z (4 months ago)
- Last Synced: 2025-02-25T05:27:42.566Z (4 months ago)
- Topics: tabular-data, tabular-deep-learning
- Language: JavaScript
- Homepage:
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
- ultimate-awesome - awesome-tdl - A curated collection of TDL (Tabular Deep Learning) resources—libraries, projects, tutorials, papers, and more—for researchers and developers in the field. (Other Lists / Julia Lists)
README
# Awesome TDL(Tabular Deep Learning)
[](https://github.com/Xatta-Trone/awesome-tdl/pulls) 
A meticulously curated compilation of outstanding libraries, projects, tutorials, research papers, and other resources centered on Tabular Deep Learning (TDL). This repository acts as a well-organized and comprehensive resource hub, crafted to assist and inspire researchers and developers delving into the domain of TDL.
Our repository is **automatically updated** with the latest **Tabular Deep Learning related research papers from arXiv**, ensuring that users have access to the most up-to-date advancements in the field. Whether you're a researcher, developer, or enthusiast, this collection provides a centralized hub for everything Tabular Deep Learning related.
## Last Updated
February 28, 2025 at 12:37:27 AM UTC
## Table of Contents
- [Awesome TDL(Tabular Deep Learning)](#awesome-tdltabular-deep-learning)
- [Table of Contents](#table-of-contents)
- [Papers](#papers)
- [Library](#library)
- [Discussion](#discussion)
- [Tutorial](#tutorial)
- [Contributing](#contributing)
- [License](#license)
- [Star History](#star-history)
## Papers
- [TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration](https://arxiv.org/abs/2502.17049)
- [A Generative Approach to Credit Prediction with Learnable Prompts for Multi-scale Temporal Representation Learning](https://arxiv.org/abs/2404.13004)
- [GeoAggregator: An Efficient Transformer Model for Geo-Spatial Tabular Data](https://arxiv.org/abs/2502.15032)
- [Convex space learning for tabular synthetic data generation](https://arxiv.org/abs/2407.09789)
- [Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class](https://arxiv.org/abs/2412.15657)
- [TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling](https://arxiv.org/abs/2410.24210)
- [LLM Embeddings for Deep Learning on Tabular Data](https://arxiv.org/abs/2502.11596)
- [Hadron Identification Prospects With Granular Calorimeters](https://arxiv.org/abs/2502.10817)
- [HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling](https://arxiv.org/abs/2403.13319)
- [Is Deep Learning finally better than Decision Trees on Tabular Data?](https://arxiv.org/abs/2402.03970)
- [Representation Learning on Out of Distribution in Tabular Data](https://arxiv.org/abs/2502.10095)
- [Application of Tabular Transformer Architectures for Operating System Fingerprinting](https://arxiv.org/abs/2502.09084)
- [Rethinking Pre-Training in Tabular Data: A Neighborhood Embedding Perspective](https://arxiv.org/abs/2311.00055)
- [SampleLLM: Optimizing Tabular Data Synthesis in Recommendations](https://arxiv.org/abs/2501.16125)
- [Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations](https://arxiv.org/abs/2502.07181)
- [SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection](https://arxiv.org/abs/2502.07119)
- [A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks](https://arxiv.org/abs/2502.06031)
- [Gradient-based Explanations for Deep Learning Survival Models](https://arxiv.org/abs/2502.04970)
- [Self-Regulation and Requesting Interventions](https://arxiv.org/abs/2502.04576)
- [Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer](https://arxiv.org/abs/2502.04573)
- [Network-Wide Traffic Flow Estimation Across Multiple Cities with Global Open Multi-Source Data: A Large-Scale Case Study in Europe and North America](https://arxiv.org/abs/2502.03798)
- [(GG) MoE vs. MLP on Tabular Data](https://arxiv.org/abs/2502.03608)
- [DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive Framework](https://arxiv.org/abs/2501.10910)
- [xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods](https://arxiv.org/abs/2502.03014)
- [Data Wrangling Task Automation Using Code-Generating Language Models](https://arxiv.org/abs/2502.15732)
- [DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models](https://arxiv.org/abs/2411.12643)
- [Less is More: Simplifying Network Traffic Classification Leveraging RFCs](https://arxiv.org/abs/2502.00586)
- [A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges](https://arxiv.org/abs/2411.18892)
- [Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring](https://arxiv.org/abs/2501.10677)
- [Random Feature Representation Boosting](https://arxiv.org/abs/2501.18283)
- [Tabular and Deep Reinforcement Learning for Gittins Index](https://arxiv.org/abs/2405.01157)
- [Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning](https://arxiv.org/abs/2501.15495)
- [DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group Robust Early Diagnosis of MCI to AD Conversion](https://arxiv.org/abs/2411.10814)
- [Multimodal Prescriptive Deep Learning](https://arxiv.org/abs/2501.14152)
- [One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data](https://arxiv.org/abs/2302.06375)
- [Table2Image: Interpretable Tabular Data Classification with Realistic Image Transformations](https://arxiv.org/abs/2412.06265)
- [Utilising Deep Learning to Elicit Expert Uncertainty](https://arxiv.org/abs/2501.11813)
- [Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data](https://arxiv.org/abs/2410.03705)
- [X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs](https://arxiv.org/abs/2304.01285)
- [Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff](https://arxiv.org/abs/2310.12671)
- [Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation](https://arxiv.org/abs/2501.09112)
- [Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data](https://arxiv.org/abs/2407.04491)
- [Graph Counterfactual Explainable AI via Latent Space Traversal](https://arxiv.org/abs/2501.08850)
- [A Closer Look at Deep Learning Methods on Tabular Datasets](https://arxiv.org/abs/2407.00956)
- [Large Language Models for Knowledge Graph Embedding Techniques, Methods, and Challenges: A Survey](https://arxiv.org/abs/2501.07766)
- [Transfer Learning of Tabular Data by Finetuning Large Language Models](https://arxiv.org/abs/2501.06863)
- [Data Augmentation for Deep Learning Regression Tasks by Machine Learning Models](https://arxiv.org/abs/2501.03654)
- [Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions](https://arxiv.org/abs/2501.03540)
- [Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks](https://arxiv.org/abs/2409.08647)
- [Segmenting Action-Value Functions Over Time-Scales in SARSA via TD(ΔΔ)](https://arxiv.org/abs/2411.14783)
## Library
## Discussion
## Tutorial
## Contributing
We welcome your contributions! Please follow these steps to contribute:
1. Fork the repo.
2. Create a new branch (e.g., `feature/new-resource`).
3. Commit your changes to the new branch.
4. Create a Pull Request, and provide a brief description of the new resources.
Please make sure that the resources you add are relevant to the field of Tabular Deep Learning. Before contributing, take a look at the existing resources to avoid duplicates.
## License
This work is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
## Star History
[](https://star-history.com/#Xatta-Trone/awesome-tdl&Date)