表格深度学习论文趋势 2023–2026

主要结论

• 表格深度学习论文从 2023 年的 150 篇降到 2026 年的 33 篇，热度在回落。
• NeurIPS 是发表格深度学习论文最多的会议，2023–2026 年共 133 篇，占四个会议总量的 62%。
• 交叉方向里涨得最快的是驾驶仿真：同时属于两个主题的论文从 2023 年的 3 篇增至 2026 年的 6 篇。
• 在「图与结构化数据」的 5 个主题里，它的 2026 年论文数排第 5；同组增长最快的时间序列是 3.2 倍，表格深度学习是 0.2 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026Coherent Routing in Decision Trees: Phase-Interference Learning for Interpretable Tabular Prediction
• ICML 2026Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
• ICLR 2026Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion
• CVPR 2026MultiModalPFN Extending Prior-Data Fitted Networks for Multimodal Tabular Learning
• NeurIPS 2025ConTextTab: A Semantics-Aware Tabular In-Context Learner
• ICML 2025Latent Score-Based Reweighting for Robust Classification on Imbalanced Tabular Data
• ICLR 2025Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later
• NeurIPS 2024WikiDBs: A Large-Scale Corpus Of Relational Databases From Wikidata
• ICML 2024BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
• ICLR 2024GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
• NeurIPS 2023Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement
• ICML 2023Compressing Tabular Data via Latent Variable Estimation
• ICLR 2023TabCaps: A Capsule Neural Network for Tabular Data Classification with BoW Routing
• NeurIPS 2026LLM-Enhanced Random Forests in Orthogonal Hyperbolic Subspaces for Tabular Learning
• ICML 2026FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
• NeurIPS 2025Feature-aware Modulation for Learning from Temporal Tabular Data
• ICML 2025TabFlex: Scaling Tabular Learning to Millions with Linear Attention
• ICLR 2025SAVA: Scalable Learning-Agnostic Data Valuation
• NeurIPS 2024Evaluating language models as risk scores
• ICML 2024Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images

全部表格深度学习论文

共 150 篇，第 1 / 3 页

1. ICLR 2023GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure
2. ICLR 2023Language Models are Realistic Tabular Data Generators
3. ICLR 2023STaSy: Score-based Tabular data Synthesis
4. ICLR 2023TabCaps: A Capsule Neural Network for Tabular Data Classification with BoW Routing
5. ICLR 2023TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
6. ICLR 2023Transfer Learning with Deep Tabular Models
7. ICML 2023Adversarial Collaborative Learning on Non-IID Features
8. ICML 2023Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
9. ICML 2023Causal Structure Learning for Latent Intervened Non-stationary Data
10. ICML 2023CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis
11. ICML 2023Compressing Tabular Data via Latent Variable Estimation
12. ICML 2023Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
13. ICML 2023Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
14. ICML 2023Do Machine Learning Models Learn Statistical Rules Inferred from Data?
15. ICML 2023FeDXL: Provable Federated Learning for Deep X-Risk Optimization
16. ICML 2023Learning Belief Representations for Partially Observable Deep RL
17. ICML 2023Learning Globally Smooth Functions on Manifolds
18. ICML 2023Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
19. ICML 2023MODeL: Memory Optimizations for Deep Learning
20. ICML 2023Nearly-tight Bounds for Deep Kernel Learning
21. ICML 2023On Generalizations of Some Distance Based Classifiers for HDLSS Data
22. ICML 2023On the Privacy-Robustness-Utility Trilemma in Distributed Learning
23. ICML 2023Personalized Federated Learning with Inferred Collaboration Graphs
24. ICML 2023Quantitative Universal Approximation Bounds for Deep Belief Networks
25. ICML 2023Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning
26. ICML 2023TabDDPM: Modelling Tabular Data with Diffusion Models
27. ICML 2023TabLeak: Tabular Data Leakage in Federated Learning
28. ICML 2023The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
29. ICML 2023Towards Controlled Data Augmentations for Active Learning
30. ICML 2023Towards Sustainable Learning: Coresets for Data-efficient Deep Learning
31. ICML 2023Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
32. ICML 2023Trompt: Towards a Better Deep Neural Network for Tabular Data
33. ICML 2023VA-learning as a more efficient alternative to Q-learning
34. ICML 2023Variational Mixture of HyperGenerators for Learning Distributions over Functions
35. ICML 2023XTab: Cross-table Pretraining for Tabular Transformers
36. NeurIPS 2023$\mathbf{\mathbb{E}^{FWI}}$: Multiparameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
37. NeurIPS 2023$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning
38. NeurIPS 2023A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
39. NeurIPS 2023A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
40. NeurIPS 2023A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space
41. NeurIPS 2023A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning
42. NeurIPS 2023A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
43. NeurIPS 2023A Variational Perspective on High-Resolution ODEs
44. NeurIPS 2023Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism
45. NeurIPS 2023Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement
46. NeurIPS 2023Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
47. NeurIPS 2023Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
48. NeurIPS 2023An Inductive Bias for Tabular Deep Learning
49. NeurIPS 2023An Information Theory Perspective on Variance-Invariance-Covariance Regularization
50. NeurIPS 2023Anchor Data Augmentation

常见问题

2023–2026 年四大会议一共有多少篇表格深度学习论文？

共 214 篇，按年份分别是 2023 年 150 篇、2024 年 17 篇、2025 年 14 篇、2026 年 33 篇。

NeurIPS 2026 有多少篇表格深度学习论文？

NeurIPS 2026 有 9 篇表格深度学习论文，上一届是 5 篇。

ICLR 2026 有多少篇表格深度学习论文？

ICLR 2026 有 1 篇表格深度学习论文，上一届是 6 篇。

ICML 2026 有多少篇表格深度学习论文？

ICML 2026 有 22 篇表格深度学习论文，上一届是 3 篇。

CVPR 2026 有多少篇表格深度学习论文？

CVPR 2026 有 1 篇表格深度学习论文。

哪个会议的表格深度学习论文最多？

NeurIPS，2023–2026 年共 133 篇。