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 74 篇，第 2 / 2 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

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。