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 5 / 27 页
- ICML 2026Generative Augmented Inference
- ICML 2026Generative Inverse Design with Abstention via Diagonal Flow Matching
- ICML 2026Generative Large Neighborhood Search: Scalable Set Cover Optimization via Discrete Diffusion
- ICML 2026Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients
- ICML 2026Generative Modeling of Irregular Time Series via SDE-Induced Continuous-Discrete Variational Inference
- ICML 2026Generative Online Reinforcement Learning
- ICML 2026Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
- ICML 2026Geodesic Flow Matching for Denoising High-Dimensional Structured Representations
- ICML 2026GeoFlow: Geo-Aware Modeling of Inter-Area Relationships in Origin-Destination Flow Prediction and Generation
- ICML 2026Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts
- ICML 2026Geometric Decoupling: Diagnosing the Structural Instability of Latent
- ICML 2026Geometry-Aware Probabilistic Circuits via Voronoi Tessellations
- ICML 2026Geometry-Aware Tabular Diffusion
- ICML 2026Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping
- ICML 2026GEPC: Group-Equivariant Posterior Consistency for Out-of-Distribution Detection in Diffusion Models
- ICML 2026GFedCL: Graph-Based Federated Continual Learning with Spatial and Temporal Awareness
- ICML 2026GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation
- ICML 2026GLAD: Bidirectional Structure-Attribute Alignment via Latent Graph Diffusion Models
- ICML 2026Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
- ICML 2026Gradient Preconditioning for Efficient and Reliable Reward-Guided Generation
- ICML 2026Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Model
- ICML 2026GraphP-FL: Personalized Federated Graph Learning via Dynamic Structure Awareness and Fisher Information Elastic Alignment
- ICML 2026GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
- ICML 2026GuidedBridge: Training-freely Improving Bridge Models with Prior Guidance
- ICML 2026Hard-Constrained Graph Generation with Discrete-Projection Diffusion
- ICML 2026HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference
- ICML 2026Heterogeneous Customizable Personalized Federated Fine-Tuning Approach for Large Language Models
- ICML 2026High-accuracy and dimension-free sampling with diffusions
- ICML 2026High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent Confounders
- ICML 2026HilbertA: Hilbert-Curve–Aligned Sparse Attention for 2D Structured Data
- ICML 2026HIVE-3D: Hierarchical Voxel Enhancement for High-Quality 3D Scene Generation
- ICML 2026Hölder++: Improving Quality-Coherence Trade-off in Multimodal VAEs
- ICML 2026How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance
- ICML 2026Hyperbolic RQ-VAE enhanced Generative Recommendation with Differential-Length Codebook Strategy
- ICML 2026Identifiable Equivariant Networks are Layerwise Equivariant
- ICML 2026IDLM: Inverse-distilled Diffusion Language Models
- ICML 2026Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness
- ICML 2026Improving Classifier-Free Guidance of Flow Matching via Manifold Projection
- ICML 2026Improving LLM-Based Recommenders with Conservative Generative Flow Networks
- ICML 2026Improving Sampling for Masked Diffusion Models via Information Gain
- ICML 2026In-Context Generation with Regional Constraints for Instructional Video Editing
- ICML 2026In-Context Universal Approximation, Compositional Generalization, and Algorithm Emulation
- ICML 2026Independent Component Discovery in Temporal Count Data
- ICML 2026InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames
- ICML 2026Inference-time optimization for experiment-grounded protein ensemble generation
- ICML 2026Infinite Mask Diffusion for Few-Step Distillation
- ICML 2026Infinite-Dimensional Generative Diffusions via Doob’s h-Transform
- ICML 2026Infinite-Precision Autoregressive Modeling for Vector Graphics and Layouts
- ICML 2026Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory
- ICML 2026Information-Geometric Adaptive Sampling for Graph Diffusion
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。