深度学习 · representation learning论文趋势 2023–2026

主要结论

• 深度学习 · representation learning论文从 2023 年的 259 篇降到 2026 年的 214 篇，热度在回落。
• NeurIPS 是发深度学习 · representation learning论文最多的会议，2023–2026 年共 279 篇，占四个会议总量的 35%。
• 交叉方向里涨得最快的是多模态大模型：同时属于两个主题的论文从 2023 年的 11 篇增至 2026 年的 28 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 7；同组增长最快的扩散模型是 4.1 倍，深度学习 · representation learning是 0.8 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 2026Active Learning as Nullspace Regulation: A Spectral Representation Perspective
• ICML 2026Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
• ICLR 2026Boosting Open Set Recognition Performance through Modulated Representation Learning
• CVPR 2026CanonCGT Reference-Based Color Grading via Canonical Pivot Representation
• NeurIPS 2025Causality-Induced Positional Encoding for Transformer-Based Representation Learning of Non-Sequential Features
• ICML 2025Banyan: Improved Representation Learning with Explicit Structure
• ICLR 2025Edge-aware Image Smoothing with Relative Wavelet Domain Representation
• CVPR 20253D-SLNR: A Super Lightweight Neural Representation for Large-scale 3D Mapping
• NeurIPS 2024GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning
• ICML 2024Data-free Neural Representation Compression with Riemannian Neural Dynamics
• ICLR 2024$\texttt{NAISR}$: A 3D Neural Additive Model for Interpretable Shape Representation
• CVPR 2024Disentangled Prompt Representation for Domain Generalization
• NeurIPS 2023[Re] CrossWalk: Fairness-enhanced Node Representation Learning
• ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
• ICLR 2023Bispectral Neural Networks
• CVPR 2023AnyFlow: Arbitrary Scale Optical Flow With Implicit Neural Representation
• NeurIPS 2026Approximation in Contrastive Representation Learning
• ICML 2026Amortized Maximum Inner Product Search with Learned Support Functions
• ICLR 2026Decoupling Dynamical Richness from Representation Learning: Towards Practical Measurement
• CVPR 2026Enhancing Visual Representation with Textual Semantics Textual Semantics-Powered Prototypes for

全部深度学习 · representation learning论文

共 182 篇，第 2 / 4 页

51. ICML 2026RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
52. ICML 2026SABER: Continual Learning with Representation Conflict Management
53. ICML 2026Seeing the Unseen: Physics-as-Representation for Generalizable Gaze Perception
54. ICML 2026Seeking Commonality, Preserving Specificity: A Spectral-Aware Hierarchical Framework for Cross-City Road Representation Learning
55. ICML 2026Separating Representation from Reconstruction Enables Scalable Text Encoders
56. ICML 2026SGERA: Stein-Guided ECG-Report Alignment for ECG Representation Learning
57. ICML 2026Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
58. ICML 2026SLAE: Strictly Local All-atom Environment for Protein Representation
59. ICML 2026Supervised Graph Contrastive Learning for Gene Regulatory Networks
60. ICML 2026TabularBERT: Binning-Based Self-Supervised Learning for Tabular Representation
61. ICML 2026The Truth Lies Somewhere in the Middle (of the Generated Tokens)
62. ICML 2026Topology-Aware Contrastive Learning: Regulating Representation Connectivity via Persistent Homology
63. ICML 2026Tree-Structured Orthonormal Decomposition of the Aitchison Simplex
64. ICML 2026Uncovering the Latent Potential of Deep Intermediate Representations
65. ICML 2026Understanding Multimodal Learning: A Loss Landscape Smoothness Perspective
66. ICML 2026Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions
67. ICML 2026Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representation
68. ICML 2026Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs
69. ICML 2026Variational Learning of Disentangled Representations
70. ICML 2026VBA: Vector Bundle Attention for Intrinsically Geometric Representation Learning
71. ICML 2026Vector Linking via Cross-Model Local Isometric Consistency
72. ICML 2026View Space: Learning Representation across Arbitrary Graphs
73. ICML 2026What Makes a Representation Good for Single-Cell Perturbation Prediction?
74. ICML 2026When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models
75. ICML 2025A Closer Look at Backdoor Attacks on CLIP
76. ICML 2025A Closer Look at Multimodal Representation Collapse
77. ICML 2025Approximating Latent Manifolds in Neural Networks via Vanishing Ideals
78. ICML 2025Attention-Only Transformers via Unrolled Subspace Denoising
79. ICML 2025Banyan: Improved Representation Learning with Explicit Structure
80. ICML 2025Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
81. ICML 2025Cover learning for large-scale topology representation
82. ICML 2025Functional Alignment Can Mislead: Examining Model Stitching
83. ICML 2025Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings
84. ICML 2025Geometric Algebra Planes: Convex Implicit Neural Volumes
85. ICML 2025GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
86. ICML 2025Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning
87. ICML 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
88. ICML 2025Learning Representations of Instruments for Partial Identification of Treatment Effects
89. ICML 2025Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective
90. ICML 2025NEAR: Neural Electromagnetic Array Response
91. ICML 2025On the Importance of Embedding Norms in Self-Supervised Learning
92. ICML 2025On the Local Complexity of Linear Regions in Deep ReLU Networks
93. ICML 2025On the Similarities of Embeddings in Contrastive Learning
94. ICML 2025Online Laplacian-Based Representation Learning in Reinforcement Learning
95. ICML 2025Point-Level Topological Representation Learning on Point Clouds
96. ICML 2025PRIME: Deep Imbalanced Regression with Proxies
97. ICML 2025Q-Supervised Contrastive Representation: A State Decoupling Framework for Safe Offline Reinforcement Learning
98. ICML 2025Random Feature Representation Boosting
99. ICML 2025Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning
100. ICML 2025Representation Preserving Multiclass Agnostic to Realizable Reduction

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · representation learning论文？

共 802 篇，按年份分别是 2023 年 259 篇、2024 年 177 篇、2025 年 152 篇、2026 年 214 篇。

NeurIPS 2026 有多少篇深度学习 · representation learning论文？

NeurIPS 2026 有 59 篇深度学习 · representation learning论文，上一届是 54 篇。

ICLR 2026 有多少篇深度学习 · representation learning论文？

ICLR 2026 有 39 篇深度学习 · representation learning论文，上一届是 37 篇。

ICML 2026 有多少篇深度学习 · representation learning论文？

ICML 2026 有 74 篇深度学习 · representation learning论文，上一届是 35 篇。

CVPR 2026 有多少篇深度学习 · representation learning论文？

CVPR 2026 有 42 篇深度学习 · representation learning论文，上一届是 26 篇。

哪个会议的深度学习 · representation learning论文最多？

NeurIPS，2023–2026 年共 279 篇。