深度学习 · generative models and autoencoders论文趋势 2023–2026
ICML/NeurIPS 深度学习方向：generative_models_and_autoencoders
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 4,314 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 776 | 78 | 286 | 76 | 1,216 |
| 2024 | 349 | 174 | 139 | 106 | 768 |
| 2025 | 240 | 275 | 275 | 110 | 900 |
| 2026 | 349 | 303 | 604 | 174 | 1,430 |
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 1,304 篇，第 21 / 27 页
- ICML 2024Stacking Deep Set Networks and Pooling by Quantiles
- ICML 2024Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
- ICML 2024Stochastic Interpolants with Data-Dependent Couplings
- ICML 2024StrWAEs to Invariant Representations
- ICML 2024Switched Flow Matching: Eliminating Singularities via Switching ODEs
- ICML 2024The Emergence of Reproducibility and Consistency in Diffusion Models
- ICML 2024The Good, The Bad, and Why: Unveiling Emotions in Generative AI
- ICML 2024The Linear Representation Hypothesis and the Geometry of Large Language Models
- ICML 2024The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright BreachesWithout Adjusting Finetuning Pipeline
- ICML 2024Timer: Generative Pre-trained Transformers Are Large Time Series Models
- ICML 2024Topological Neural Networks go Persistent, Equivariant, and Continuous
- ICML 2024Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components
- ICML 2024Towards Neural Architecture Search through Hierarchical Generative Modeling
- ICML 2024Towards Scalable and Versatile Weight Space Learning
- ICML 2024Towards Theoretical Understandings of Self-Consuming Generative Models
- ICML 2024Transformers, parallel computation, and logarithmic depth
- ICML 2024Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
- ICML 2024Why do Variational Autoencoders Really Promote Disentanglement?
- ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
- ICML 2023A Flexible Diffusion Model
- ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
- ICML 2023A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
- ICML 2023A Mathematical Model for Curriculum Learning for Parities
- ICML 2023A Modern Look at the Relationship between Sharpness and Generalization
- ICML 2023A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
- ICML 2023A Statistical Perspective on Retrieval-Based Models
- ICML 2023A theory of continuous generative flow networks
- ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
- ICML 2023abess: A Fast Best-Subset Selection Library in Python and R
- ICML 2023Action Matching: Learning Stochastic Dynamics from Samples
- ICML 2023AdaBoost is not an Optimal Weak to Strong Learner
- ICML 2023AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
- ICML 2023Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
- ICML 2023Adaptive IMLE for Few-shot Pretraining-free Generative Modelling
- ICML 2023Adversarial Cheap Talk
- ICML 2023Adversarial Learning of Distributional Reinforcement Learning
- ICML 2023An SDE for Modeling SAM: Theory and Insights
- ICML 2023Analyzing Diffusion as Serial Reproduction
- ICML 2023Attributing Image Generative Models using Latent Fingerprints
- ICML 2023Bayes-optimal Learning of Deep Random Networks of Extensive-width
- ICML 2023Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts
- ICML 2023Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
- ICML 2023Better Diffusion Models Further Improve Adversarial Training
- ICML 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectories
- ICML 2023BiBench: Benchmarking and Analyzing Network Binarization
- ICML 2023Bidirectional Learning for Offline Model-based Biological Sequence Design
- ICML 2023Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers
- ICML 2023Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
- ICML 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
- ICML 2023Brauer's Group Equivariant Neural Networks
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查深度学习 · generative models and autoencoders在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "dl-generative-models-and-autoencoders"}
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。