深度学习 · generative models and autoencoders论文趋势 2023–2026

主要结论

• 深度学习 · generative models and autoencoders论文从 2023 年的 1,216 篇增长到 2026 年的 1,430 篇，三年间扩大到约 1.2 倍。
• NeurIPS 是发深度学习 · generative models and autoencoders论文最多的会议，2023–2026 年共 1,714 篇，占四个会议总量的 40%。
• 交叉方向里涨得最快的是图像生成：同时属于两个主题的论文从 2023 年的 35 篇增至 2026 年的 150 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 2；同组增长最快的扩散模型是 4.1 倍，深度学习 · generative models and autoencoders是 1.2 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 2026A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs
• ICML 2026(1D) Ordered Tokens Enable Efficient Test-Time Search
• ICLR 2026``Noisier'’ Noise Contrastive Estimation is (Almost) Maximum Likelihood
• CVPR 2026Anchoring and Rescaling Attention for Semantically Coherent Inbetweening
• NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
• ICML 2025A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
• ICLR 2025A Watermark for Order-Agnostic Language Models
• CVPR 2025DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Fixed-Point Approach for Causal Generative Modeling
• ICLR 2024$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
• CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
• NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
• ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
• ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
• CVPR 20233D GAN Inversion With Facial Symmetry Prior
• NeurIPS 2026Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
• ICML 2026$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
• ICLR 2026Analyzing and Evaluating Unbiased Language Model Watermark
• CVPR 2026Bidirectional Normalizing Flow From Data to Noise and Back

全部深度学习 · generative models and autoencoders论文

共 286 篇，第 1 / 6 页

1. ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
2. ICML 2023A Flexible Diffusion Model
3. ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
4. ICML 2023A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
5. ICML 2023A Mathematical Model for Curriculum Learning for Parities
6. ICML 2023A Modern Look at the Relationship between Sharpness and Generalization
7. ICML 2023A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
8. ICML 2023A Statistical Perspective on Retrieval-Based Models
9. ICML 2023A theory of continuous generative flow networks
10. ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
11. ICML 2023abess: A Fast Best-Subset Selection Library in Python and R
12. ICML 2023Action Matching: Learning Stochastic Dynamics from Samples
13. ICML 2023AdaBoost is not an Optimal Weak to Strong Learner
14. ICML 2023AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
15. ICML 2023Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
16. ICML 2023Adaptive IMLE for Few-shot Pretraining-free Generative Modelling
17. ICML 2023Adversarial Cheap Talk
18. ICML 2023Adversarial Learning of Distributional Reinforcement Learning
19. ICML 2023An SDE for Modeling SAM: Theory and Insights
20. ICML 2023Analyzing Diffusion as Serial Reproduction
21. ICML 2023Attributing Image Generative Models using Latent Fingerprints
22. ICML 2023Bayes-optimal Learning of Deep Random Networks of Extensive-width
23. ICML 2023Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts
24. ICML 2023Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
25. ICML 2023Better Diffusion Models Further Improve Adversarial Training
26. ICML 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectories
27. ICML 2023BiBench: Benchmarking and Analyzing Network Binarization
28. ICML 2023Bidirectional Learning for Offline Model-based Biological Sequence Design
29. ICML 2023Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers
30. ICML 2023Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
31. ICML 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
32. ICML 2023Brauer's Group Equivariant Neural Networks
33. ICML 2023Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
34. ICML 2023Calibrating Multimodal Learning
35. ICML 2023CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data
36. ICML 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
37. ICML 2023Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
38. ICML 2023CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
39. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
40. ICML 2023Composer: Creative and Controllable Image Synthesis with Composable Conditions
41. ICML 2023Compositional Exemplars for In-context Learning
42. ICML 2023Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
43. ICML 2023Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
44. ICML 2023ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
45. ICML 2023Conditionally Strongly Log-Concave Generative Models
46. ICML 2023Conditions and Assumptions for Constraint-based Causal Structure Learning
47. ICML 2023Consistency Models
48. ICML 2023Continuation Path Learning for Homotopy Optimization
49. ICML 2023ContraBAR: Contrastive Bayes-Adaptive Deep RL
50. ICML 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · generative models and autoencoders论文？

共 4,314 篇，按年份分别是 2023 年 1,216 篇、2024 年 768 篇、2025 年 900 篇、2026 年 1,430 篇。

NeurIPS 2026 有多少篇深度学习 · generative models and autoencoders论文？

NeurIPS 2026 有 349 篇深度学习 · generative models and autoencoders论文，上一届是 240 篇。

ICLR 2026 有多少篇深度学习 · generative models and autoencoders论文？

ICLR 2026 有 303 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。

ICML 2026 有多少篇深度学习 · generative models and autoencoders论文？

ICML 2026 有 604 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。

CVPR 2026 有多少篇深度学习 · generative models and autoencoders论文？

CVPR 2026 有 174 篇深度学习 · generative models and autoencoders论文，上一届是 110 篇。

哪个会议的深度学习 · generative models and autoencoders论文最多？

NeurIPS，2023–2026 年共 1,714 篇。