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 35 篇，第 1 / 1 页

1. ICML 2025A Closer Look at Backdoor Attacks on CLIP
2. ICML 2025A Closer Look at Multimodal Representation Collapse
3. ICML 2025Approximating Latent Manifolds in Neural Networks via Vanishing Ideals
4. ICML 2025Attention-Only Transformers via Unrolled Subspace Denoising
5. ICML 2025Banyan: Improved Representation Learning with Explicit Structure
6. ICML 2025Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
7. ICML 2025Cover learning for large-scale topology representation
8. ICML 2025Functional Alignment Can Mislead: Examining Model Stitching
9. ICML 2025Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings
10. ICML 2025Geometric Algebra Planes: Convex Implicit Neural Volumes
11. ICML 2025GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
12. ICML 2025Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning
13. ICML 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
14. ICML 2025Learning Representations of Instruments for Partial Identification of Treatment Effects
15. ICML 2025Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective
16. ICML 2025NEAR: Neural Electromagnetic Array Response
17. ICML 2025On the Importance of Embedding Norms in Self-Supervised Learning
18. ICML 2025On the Local Complexity of Linear Regions in Deep ReLU Networks
19. ICML 2025On the Similarities of Embeddings in Contrastive Learning
20. ICML 2025Online Laplacian-Based Representation Learning in Reinforcement Learning
21. ICML 2025Point-Level Topological Representation Learning on Point Clouds
22. ICML 2025PRIME: Deep Imbalanced Regression with Proxies
23. ICML 2025Q-Supervised Contrastive Representation: A State Decoupling Framework for Safe Offline Reinforcement Learning
24. ICML 2025Random Feature Representation Boosting
25. ICML 2025Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning
26. ICML 2025Representation Preserving Multiclass Agnostic to Realizable Reduction
27. ICML 2025sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models
28. ICML 2025Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows
29. ICML 2025Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning
30. ICML 2025Stabilizing Sample Similarity in Representation via Mitigating Random Consistency
31. ICML 2025Stable Fair Graph Representation Learning with Lipschitz Constraint
32. ICML 2025Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs
33. ICML 2025The Complexity of Learning Sparse Superposed Features with Feedback
34. ICML 2025The Generalized Skew Spectrum of Graphs
35. ICML 2025When do neural networks learn world 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。