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 133 篇，第 2 / 3 页

51. NeurIPS 2023Data-Informed Geometric Space Selection
52. NeurIPS 2023Deep Fractional Fourier Transform
53. NeurIPS 2023Deep Insights into Noisy Pseudo Labeling on Graph Data
54. NeurIPS 2023Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration
55. NeurIPS 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
56. NeurIPS 2023Differentiable sorting for censored time-to-event data.
57. NeurIPS 2023Differentially Private Image Classification by Learning Priors from Random Processes
58. NeurIPS 2023Distributed Personalized Empirical Risk Minimization
59. NeurIPS 2023DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning
60. NeurIPS 2023Feature Learning for Interpretable, Performant Decision Trees
61. NeurIPS 2023Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
62. NeurIPS 2023Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
63. NeurIPS 2023Flow Factorized Representation Learning
64. NeurIPS 2023GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference
65. NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
66. NeurIPS 2023Geometric Transformer with Interatomic Positional Encoding
67. NeurIPS 2023Gradient-Based Feature Learning under Structured Data
68. NeurIPS 2023Harnessing Hard Mixed Samples with Decoupled Regularizer
69. NeurIPS 2023High dimensional, tabular deep learning with an auxiliary knowledge graph
70. NeurIPS 2023HyTrel: Hypergraph-enhanced Tabular Data Representation Learning
71. NeurIPS 2023Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data
72. NeurIPS 2023Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes
73. NeurIPS 2023Joint Data-Task Generation for Auxiliary Learning
74. NeurIPS 2023Joint Training of Deep Ensembles Fails Due to Learner Collusion
75. NeurIPS 2023Labeling Neural Representations with Inverse Recognition
76. NeurIPS 2023Language Models are Weak Learners
77. NeurIPS 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
78. NeurIPS 2023LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing
79. NeurIPS 2023Meet in the Middle: A New Pre-training Paradigm
80. NeurIPS 2023Mitigating the Effect of Incidental Correlations on Part-based Learning
81. NeurIPS 2023NCDL: A Framework for Deep Learning on non-Cartesian Lattices
82. NeurIPS 2023New Bounds for Hyperparameter Tuning of Regression Problems Across Instances
83. NeurIPS 2023Noether Embedding: Efficient Learning of Temporal Regularities
84. NeurIPS 2023Offline Imitation Learning with Variational Counterfactual Reasoning
85. NeurIPS 2023On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data
86. NeurIPS 2023On Imitation in Mean-field Games
87. NeurIPS 2023On Single-Index Models beyond Gaussian Data
88. NeurIPS 2023On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
89. NeurIPS 2023On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets
90. NeurIPS 2023On the Overlooked Structure of Stochastic Gradients
91. NeurIPS 2023On the Robustness of Removal-Based Feature Attributions
92. NeurIPS 2023On the Role of Randomization in Adversarially Robust Classification
93. NeurIPS 2023One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning
94. NeurIPS 2023OpenDataVal: a Unified Benchmark for Data Valuation
95. NeurIPS 2023Operator Learning with Neural Fields: Tackling PDEs on General Geometries
96. NeurIPS 2023Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
97. NeurIPS 2023PAC Learning Linear Thresholds from Label Proportions
98. NeurIPS 2023Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck
99. NeurIPS 2023Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
100. NeurIPS 2023Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds

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。