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 1 / 2 页

1. ICML 2026Action-Sufficient Goal Representations
2. ICML 2026Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
3. ICML 2026Adversarial Robustness of Implicit Neural Representation-Based Classifiers
4. ICML 2026Amortized Maximum Inner Product Search with Learned Support Functions
5. ICML 2026Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
6. ICML 2026Bridging Functional and Representational Similarity via Usable Information
7. ICML 2026Calibrated Multimodal Representation Learning with Missing Modalities
8. ICML 2026Can Large Language Models Generalize Procedures Across Representations?
9. ICML 2026Cross-Tactile Sensor Representation Learning
10. ICML 2026Deep Ensemble Clustering for Visual Representation Learning
11. ICML 2026Demystifying When Pruning Works via Representation Hierarchies
12. ICML 2026Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
13. ICML 2026From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data
14. ICML 2026From Generalist to Specialist Representation
15. ICML 2026From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
16. ICML 2026Geodesic Calculus on Implicitly Defined Latent Manifolds
17. ICML 2026Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
18. ICML 2026Grounding Functional Similarity by Invariance-Aware Model Stitching
19. ICML 2026Grouter: Decoupling Routing from Representation for Accelerated MoE Training
20. ICML 2026H$^2$CL: Heterogeneity-Aware Hypergraph Contrastive Learning for Robust Representation Learning
21. ICML 2026Hierarchical Goal Abstractions via Learned Subset Relations
22. ICML 2026Hierarchical Successor Representation for Robust Transfer
23. ICML 2026How Should Transformers Encode Numeric Values in Electronic Health Records?
24. ICML 2026Identifiable Equivariant Networks are Layerwise Equivariant
25. ICML 2026INFER: Learning Implicit Neural Frequency Response Fields for Confined Acoustic Environments
26. ICML 2026Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising
27. ICML 2026Joint Learning in the Gaussian Single Index Model
28. ICML 2026Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
29. ICML 2026Learning Global Representation from Queries for Vectorized HD Map Construction
30. ICML 2026Learning High-Frequency Continuous Action Chunks in Latent Space
31. ICML 2026Learning Molecular Semantic Invariant Representation with Prototype Constraint
32. ICML 2026Learning the Minimum Action Distance
33. ICML 2026Learning to Perceive the World Through Control: Empowerment-Based Representation Learning
34. ICML 2026Localized, High-resolution Geographic Representations with Slepian Functions
35. ICML 2026Logit Distance Bounds Representational Similarity
36. ICML 2026Multi-Adapter Representation Interventions via Energy Calibration
37. ICML 2026Multiview Self-Representation Learning across Heterogeneous Views
38. ICML 2026nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding
39. ICML 2026Neural Feature Geometry Evolves as Discrete Ricci Flow
40. ICML 2026Nonconvex Low-Rank Tensor Representation with Deep Priors for Multiview Subspace Clustering
41. ICML 2026PatternKV: Flattening KV Representation Expands Quantization Headroom
42. ICML 2026Provable Accuracy Collapse of Embedding-Based Representations under Dimensionality Mismatch
43. ICML 2026QuITE: Query-Based Irregular Time Series Embedding
44. ICML 2026R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning
45. ICML 2026Reference-Free Meta-Learning for Generalized Implicit Neural Representation in Efficient MRI Reconstruction
46. ICML 2026Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics
47. ICML 2026Representation Drift Compensation: A Near-Zero Inference Cost Enhancement for LLM Decomposition
48. ICML 2026Representation Learning for Equivariant Inference with Guarantees
49. ICML 2026Representation Unlearning: Forgetting through Information Compression
50. ICML 2026Revisiting the Platonic Representation Hypothesis: An Aristotelian View

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。