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 214 篇，第 2 / 5 页

51. ICLR 2026Diffusion Transformers with Representation Autoencoders
52. ICLR 2026Disentangled Representation Learning for Parametric Partial Differential Equations
53. ICLR 2026Dissecting Representation Misalignment in Contrastive Learning via Influence Function
54. ICLR 2026Fair Conformal Classification via Learning Representation-Based Groups
55. ICLR 2026Gauge-invariant representation holonomy
56. ICLR 2026Generalization of Diffusion Models Arises with a Balanced Representation Space
57. ICLR 2026Greater than the Sum of Its Parts: Building Substructure into Protein Encoding Models
58. ICLR 2026Improving Set Function Approximation with Quasi-Arithmetic Neural Networks
59. ICLR 2026Information Shapes Koopman Representation
60. ICLR 2026KDP: Simplifying Representation Dynamics in Kernel Space
61. ICLR 2026Learning Molecular Chirality via Chiral Determinant Kernels
62. ICLR 2026MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale
63. ICLR 2026Nef-Net v2: Adapting Electrocardio Panorama in the wild
64. ICLR 2026PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
65. ICLR 2026PALC: Preference Alignment via Logit Calibration
66. ICLR 2026Paradigm Shift of GNN Explainer from Label Space to Prototypical Representation Space
67. ICLR 2026Proper Velocity Neural Networks
68. ICLR 2026Readout Representation: Redefining Neural Codes by Input Recovery
69. ICLR 2026Representation Alignment for Diffusion Transformers without External Components
70. ICLR 2026Representing local protein environments with machine learning force fields
71. ICLR 2026RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation
72. ICLR 2026Simplicial Embeddings Improve Sample Efficiency in Actor–Critic Agents
73. ICLR 2026Stability Under Scrutiny: Benchmarking Representation Paradigms for Online HD Mapping
74. ICLR 2026Temporal Graph Thumbnail: Robust Representation Learning with Global Evolutionary Skeleton
75. ICLR 2026The Deleuzian Representation Hypothesis
76. ICLR 2026Tokenizing Single-Channel EEG with Time-Frequency Motif Learning
77. ICLR 2026Topology Matters in RTL Circuit Representation Learning
78. ICLR 2026Unsupervised Representation Learning - an Invariant Risk Minimization Perspective
79. ICLR 2026Unveiling the Mechanism of Continuous Representation Full-Waveform Inversion: A Wave Based Neural Tangent Kernel Framework
80. ICLR 2026What matters for Representation Alignment: Global Information or Spatial Structure?
81. ICLR 2026Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
82. ICML 2026Action-Sufficient Goal Representations
83. ICML 2026Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
84. ICML 2026Adversarial Robustness of Implicit Neural Representation-Based Classifiers
85. ICML 2026Amortized Maximum Inner Product Search with Learned Support Functions
86. ICML 2026Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
87. ICML 2026Bridging Functional and Representational Similarity via Usable Information
88. ICML 2026Calibrated Multimodal Representation Learning with Missing Modalities
89. ICML 2026Can Large Language Models Generalize Procedures Across Representations?
90. ICML 2026Cross-Tactile Sensor Representation Learning
91. ICML 2026Deep Ensemble Clustering for Visual Representation Learning
92. ICML 2026Demystifying When Pruning Works via Representation Hierarchies
93. ICML 2026Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
94. ICML 2026From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data
95. ICML 2026From Generalist to Specialist Representation
96. ICML 2026From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
97. ICML 2026Geodesic Calculus on Implicitly Defined Latent Manifolds
98. ICML 2026Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
99. ICML 2026Grounding Functional Similarity by Invariance-Aware Model Stitching
100. ICML 2026Grouter: Decoupling Routing from Representation for Accelerated MoE Training

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。