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.
September 9, 2025 at 12:40:01 AM UTC
- TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration
- A Generative Approach to Credit Prediction with Learnable Prompts for Multi-scale Temporal Representation Learning
- GeoAggregator: An Efficient Transformer Model for Geo-Spatial Tabular Data
- Convex space learning for tabular synthetic data generation
- Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
- TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
- LLM Embeddings for Deep Learning on Tabular Data
- Hadron Identification Prospects With Granular Calorimeters
- HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling
- Is Deep Learning finally better than Decision Trees on Tabular Data?
- Representation Learning on Out of Distribution in Tabular Data
- Application of Tabular Transformer Architectures for Operating System Fingerprinting
- Rethinking Pre-Training in Tabular Data: A Neighborhood Embedding Perspective
- SampleLLM: Optimizing Tabular Data Synthesis in Recommendations
- Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations
- SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection
- A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks
- Gradient-based Explanations for Deep Learning Survival Models
- Self-Regulation and Requesting Interventions
- Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer
- Network-Wide Traffic Flow Estimation Across Multiple Cities with Global Open Multi-Source Data: A Large-Scale Case Study in Europe and North America
- (GG) MoE vs. MLP on Tabular Data
- DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive Framework
- xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods
- Data Wrangling Task Automation Using Code-Generating Language Models
- DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models
- Less is More: Simplifying Network Traffic Classification Leveraging RFCs
- A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
- Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring
- Random Feature Representation Boosting
- Tabular and Deep Reinforcement Learning for Gittins Index
- Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning
- DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group Robust Early Diagnosis of MCI to AD Conversion
- Multimodal Prescriptive Deep Learning
- One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data
- Table2Image: Interpretable Tabular Data Classification with Realistic Image Transformations
- Utilising Deep Learning to Elicit Expert Uncertainty
- Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data
- X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs
- Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff
- Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation
- Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data
- Graph Counterfactual Explainable AI via Latent Space Traversal
- A Closer Look at Deep Learning Methods on Tabular Datasets
- Large Language Models for Knowledge Graph Embedding Techniques, Methods, and Challenges: A Survey
- Transfer Learning of Tabular Data by Finetuning Large Language Models
- Data Augmentation for Deep Learning Regression Tasks by Machine Learning Models
- Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions
- Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks
- Segmenting Action-Value Functions Over Time-Scales in SARSA via TD(ΔΔ)
- A Binary Classification Social Network Dataset for Graph Machine Learning
- Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later
- Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners
- Synthesizing Tabular Data Using Selectivity Enhanced Generative Adversarial Networks
- Multimodal Learning for Just-In-Time Software Defect Prediction in Autonomous Driving Systems
- Understanding the Limits of Deep Tabular Methods with Temporal Shift
- TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization
- Deep-Bench: Deep Learning Benchmark Dataset for Code Generation
- Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints
- On-device edge learning for IoT data streams: a survey
- You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Time
- Learning Decision Trees as Amortized Structure Inference
- A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond
- Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists
- TabNSA: Native Sparse Attention for Efficient Tabular Data Learning
- Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning
- Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet
- GFSNetwork: Differentiable Feature Selection via Gumbel-Sigmoid Relaxation
- HyConEx: Hypernetwork classifier with counterfactual explanations
- Multi-modal Time Series Analysis: A Tutorial and Survey
- Continual Contrastive Learning on Tabular Data with Out of Distribution
- Sequence Analysis Using the Bezier Curve
- Neural-Guided Equation Discovery
- Deep Q-Learning with Gradient Target Tracking
- Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning
- Mambular: A Sequential Model for Tabular Deep Learning
- How to RETIRE Tabular Data in Favor of Discrete Digital Signal Representation
- Accelerating Task Generalisation with Multi-Level Skill Hierarchies
- FeRG-LLM : Feature Engineering by Reason Generation Large Language Models
- Explanation Space: A New Perspective into Time Series Interpretability
- Occam Gradient Descent
- Boosting Relational Deep Learning with Pretrained Tabular Models
- Loss Functions in Deep Learning: A Comprehensive Review
- TabKAN: Advancing Tabular Data Analysis using Kolmograv-Arnold Network
- Going beyond explainability in multi-modal stroke outcome prediction models
- P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data
- Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
- NRGBoost: Energy-Based Generative Boosted Trees
- No Imputation of Missing Values In Tabular Data Classification Using Incremental Learning
- Representation Learning for Tabular Data: A Comprehensive Survey
- A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography
- VisTabNet: Adapting Vision Transformers for Tabular Data
- Deep Learning with Pretrained 'Internal World' Layers: A Gemma 3-Based Modular Architecture for Wildfire Prediction
- Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
- Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction
- MDD-LLM: Towards Accuracy Large Language Models for Major Depressive Disorder Diagnosis
- TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network
- T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data
- Not Another Imputation Method: A Transformer-based Model for Missing Values in Tabular Datasets
- An Invitation to Deep Reinforcement Learning
- DetoxAI: a Python Toolkit for Debiasing Deep Learning Models in Computer Vision
- Deeply Explainable Artificial Neural Network
- Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning
- A Step towards Interpretable Multimodal AI Models with MultiFIX
- ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data
- When majority rules, minority loses: bias amplification of gradient descent
- Graph Conditional Flow Matching for Relational Data Generation
- Flexible Heteroscedastic Count Regression with Deep Double Poisson Networks
- WikiDBGraph: Large-Scale Database Graph of Wikidata for Collaborative Learning
- Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
- Realistic Evaluation of TabPFN v2 in Open Environments
- TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations
- TabPFN: One Model to Rule Them All?
- Distributionally Robust Deep Q-Learning
- Intrinsic User-Centric Interpretability through Global Mixture of Experts
- TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction
- X2Graph for Cancer Subtyping Prediction on Biological Tabular Data
- ShaTS: A Shapley-based Explainability Method for Time Series Artificial Intelligence Models applied to Anomaly Detection in Industrial Internet of Things
- Reinforcement Learning for Hanabi
- On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
- Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection
- A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
- Random at First, Fast at Last: NTK-Guided Fourier Pre-Processing for Tabular DL
- From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs
- Applying MambaAttention, TabPFN, and TabTransformers to Classify SAE Automation Levels in Crashes
- Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion
- Simple Calibration via Geodesic Kernels
- AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents
- Synthetic Tabular Data: Methods, Attacks and Defenses
- Learning based on neurovectors for tabular data: a new neural network approach
- Benchmarking Early Agitation Prediction in Community-Dwelling People with Dementia Using Multimodal Sensors and Machine Learning
- On Finetuning Tabular Foundation Models
- Local MDI+: Local Feature Importances for Tree-Based Models
- IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)
- Anomaly Detection and Generation with Diffusion Models: A Survey
- Uncertainty Prioritized Experience Replay
- CFMI: Flow Matching for Missing Data Imputation
- ConTextTab: A Semantics-Aware Tabular In-Context Learner
- A Comparative Analysis of Influence Signals for Data Debugging
- DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
- Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning
- Does DQN Learn?
- Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems
- TabArena: A Living Benchmark for Machine Learning on Tabular Data
- Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures
- ReBoot: Encrypted Training of Deep Neural Networks with CKKS Bootstrapping
- Explaining deep neural network models for electricity price forecasting with XAI
- dreaMLearning: Data Compression Assisted Machine Learning
- Enhancing Insider Threat Detection Using User-Based Sequencing and Transformer Encoders
- MOE-Enhanced Explanable Deep Manifold Transformation for Complex Data Embedding and Visualization
- Towards a unified scheme of blazar evolution
- Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)
- FACT: the Features At Convergence Theorem for neural networks
- Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data
- Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks
- User-Based Sequential Modeling with Transformer Encoders for Insider Threat Detection
- A Study of Value-Aware Eigenoptions
- A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models
- Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?
- Federated Reinforcement Learning in Heterogeneous Environments
- Unmasking Trees for Tabular Data
- Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection
- Improving the Computational Efficiency and Explainability of GeoAggregator
- RegScore: Scoring Systems for Regression Tasks
- Information Extraction from Unstructured data using Augmented-AI and Computer Vision
- Bootstrapped Reward Shaping
- Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates
- Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
- v-PuNNs: van der Put Neural Networks for Transparent Ultrametric Representation Learning
- Structural Equation-VAE: Disentangled Latent Representations for Tabular Data
- FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
- On the effectiveness of multimodal privileged knowledge distillation in two vision transformer based diagnostic applications
- Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
- Deep and diverse population synthesis for multi-person households using generative models
- Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?
- Value Function Initialization for Knowledge Transfer and Jump-start in Deep Reinforcement Learning
- Generating Feasible and Diverse Synthetic Populations Using Diffusion Models
- GNN-based Unified Deep Learning
- MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts
- Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
- Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
- Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions
- Foe for Fraud: Transferable Adversarial Attacks in Credit Card Fraud Detection
- Deep Learning for School Dropout Detection: A Comparison of Tabular and Graph-Based Models for Predicting At-Risk Students
- Imputation Not Required in Incremental Learning of Tabular Data with Missing Values
- Imputation is Not Required: Incremental Feature Attention Learning of Tabular Data with Missing Values
- TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression
- Aligning Distributionally Robust Optimization with Practical Deep Learning Needs
- Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification
- Tabular Diffusion Counterfactual Explanations
- Missing Data Imputation using Neural Cellular Automata
- Optimized Weight Initialization on the Stiefel Manifold for Deep ReLU Neural Networks
- LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence
- Unveiling the Role of Data Uncertainty in Tabular Deep Learning
- Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer
- Synthetic Survival Data Generation for Heart Failure Prognosis Using Deep Generative Models
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