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 42 篇，第 1 / 1 页

1. ICML 2024A Distributional Analogue to the Successor Representation
2. ICML 2024A Sparsity Principle for Partially Observable Causal Representation Learning
3. ICML 2024Autoencoding Conditional Neural Processes for Representation Learning
4. ICML 2024Causal Representation Learning from Multiple Distributions: A General Setting
5. ICML 2024Causal Representation Learning Made Identifiable by Grouping of Observational Variables
6. ICML 2024Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning
7. ICML 2024Data-free Neural Representation Compression with Riemannian Neural Dynamics
8. ICML 2024Deep Regression Representation Learning with Topology
9. ICML 2024Denoising Autoregressive Representation Learning
10. ICML 2024Differentiable Mapper for Topological Optimization of Data Representation
11. ICML 2024Differentially Private Representation Learning via Image Captioning
12. ICML 2024Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
13. ICML 2024Evolution-Inspired Loss Functions for Protein Representation Learning
14. ICML 2024Feasibility Consistent Representation Learning for Safe Reinforcement Learning
15. ICML 2024Federated Representation Learning in the Under-Parameterized Regime
16. ICML 2024Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries
17. ICML 2024Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks
18. ICML 2024GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
19. ICML 2024Graph2Tac: Online Representation Learning of Formal Math Concepts
20. ICML 2024Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
21. ICML 2024In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
22. ICML 2024Isometric Representation Learning for Disentangled Latent Space of Diffusion Models
23. ICML 2024Learning Graph Representation via Graph Entropy Maximization
24. ICML 2024Learning Scale-Aware Spatio-temporal Implicit Representation for Event-based Motion Deblurring
25. ICML 2024Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias
26. ICML 2024LLM-Empowered State Representation for Reinforcement Learning
27. ICML 2024Mol-AE: Auto-Encoder Based Molecular Representation Learning With 3D Cloze Test Objective
28. ICML 2024Polynomial-based Self-Attention for Table Representation Learning
29. ICML 2024Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
30. ICML 2024Position: The Platonic Representation Hypothesis
31. ICML 2024Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains
32. ICML 2024Recurrent Distance Filtering for Graph Representation Learning
33. ICML 2024Rejuvenating image-GPT as Strong Visual Representation Learners
34. ICML 2024RMIB: Representation Matching Information Bottleneck for Matching Text Representations
35. ICML 2024Robustness of Nonlinear Representation Learning
36. ICML 2024SCoRe: Submodular Combinatorial Representation Learning
37. ICML 2024Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
38. ICML 2024TSLANet: Rethinking Transformers for Time Series Representation Learning
39. ICML 2024UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
40. ICML 2024Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms
41. ICML 2024Visual Representation Learning with Stochastic Frame Prediction
42. ICML 2024When Representations Align: Universality in Representation Learning Dynamics

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。