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 4,314 篇，第 85 / 87 页
- NeurIPS 2023STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
- NeurIPS 2023Strategic Apple Tasting
- NeurIPS 2023STREAMER: Streaming Representation Learning and Event Segmentation in a Hierarchical Manner
- NeurIPS 2023Streaming Factor Trajectory Learning for Temporal Tensor Decomposition
- NeurIPS 2023StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
- NeurIPS 2023Structural Pruning for Diffusion Models
- NeurIPS 2023Structured Federated Learning through Clustered Additive Modeling
- NeurIPS 2023Structured Neural Networks for Density Estimation and Causal Inference
- NeurIPS 2023Structured State Space Models for In-Context Reinforcement Learning
- NeurIPS 2023StyleGAN knows Normal, Depth, Albedo, and More
- NeurIPS 2023Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
- NeurIPS 2023SwiFT: Swin 4D fMRI Transformer
- NeurIPS 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
- NeurIPS 2023Synthcity: a benchmark framework for diverse use cases of tabular synthetic data
- NeurIPS 2023Synthetic Experience Replay
- NeurIPS 2023T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization
- NeurIPS 2023TabMT: Generating tabular data with masked transformers
- NeurIPS 2023Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system
- NeurIPS 2023TART: A plug-and-play Transformer module for task-agnostic reasoning
- NeurIPS 2023Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
- NeurIPS 2023Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
- NeurIPS 2023Task-aware world model learning with meta weighting via bi-level optimization
- NeurIPS 2023TaskMet: Task-driven Metric Learning for Model Learning
- NeurIPS 2023Taylor TD-learning
- NeurIPS 2023Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
- NeurIPS 2023Temporal Dynamic Quantization for Diffusion Models
- NeurIPS 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
- NeurIPS 2023Tester-Learners for Halfspaces: Universal Algorithms
- NeurIPS 2023Text-to-Image Diffusion Models are Zero Shot Classifiers
- NeurIPS 2023Textually Pretrained Speech Language Models
- NeurIPS 2023The Adversarial Consistency of Surrogate Risks for Binary Classification
- NeurIPS 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
- NeurIPS 2023The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
- NeurIPS 2023The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
- NeurIPS 2023The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
- NeurIPS 2023The geometry of hidden representations of large transformer models
- NeurIPS 2023The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
- NeurIPS 2023The probability flow ODE is provably fast
- NeurIPS 2023The Quantization Model of Neural Scaling
- NeurIPS 2023The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit
- NeurIPS 2023The Transient Nature of Emergent In-Context Learning in Transformers
- NeurIPS 2023Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
- NeurIPS 2023Theoretical and Practical Perspectives on what Influence Functions Do
- NeurIPS 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
- NeurIPS 2023Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
- NeurIPS 2023Toolformer: Language Models Can Teach Themselves to Use Tools
- NeurIPS 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
- NeurIPS 2023Tools for Verifying Neural Models' Training Data
- NeurIPS 2023Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification
- NeurIPS 2023Topological Obstructions and How to Avoid Them
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。