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 1,216 篇，第 23 / 25 页

1101. NeurIPS 2023STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
1102. NeurIPS 2023Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
1103. NeurIPS 2023STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
1104. NeurIPS 2023Strategic Apple Tasting
1105. NeurIPS 2023STREAMER: Streaming Representation Learning and Event Segmentation in a Hierarchical Manner
1106. NeurIPS 2023Streaming Factor Trajectory Learning for Temporal Tensor Decomposition
1107. NeurIPS 2023StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
1108. NeurIPS 2023Structural Pruning for Diffusion Models
1109. NeurIPS 2023Structured Federated Learning through Clustered Additive Modeling
1110. NeurIPS 2023Structured Neural Networks for Density Estimation and Causal Inference
1111. NeurIPS 2023Structured State Space Models for In-Context Reinforcement Learning
1112. NeurIPS 2023StyleGAN knows Normal, Depth, Albedo, and More
1113. NeurIPS 2023Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
1114. NeurIPS 2023SwiFT: Swin 4D fMRI Transformer
1115. NeurIPS 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
1116. NeurIPS 2023Synthcity: a benchmark framework for diverse use cases of tabular synthetic data
1117. NeurIPS 2023Synthetic Experience Replay
1118. NeurIPS 2023T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization
1119. NeurIPS 2023TabMT: Generating tabular data with masked transformers
1120. NeurIPS 2023Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system
1121. NeurIPS 2023TART: A plug-and-play Transformer module for task-agnostic reasoning
1122. NeurIPS 2023Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
1123. NeurIPS 2023Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
1124. NeurIPS 2023Task-aware world model learning with meta weighting via bi-level optimization
1125. NeurIPS 2023TaskMet: Task-driven Metric Learning for Model Learning
1126. NeurIPS 2023Taylor TD-learning
1127. NeurIPS 2023Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
1128. NeurIPS 2023Temporal Dynamic Quantization for Diffusion Models
1129. NeurIPS 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
1130. NeurIPS 2023Tester-Learners for Halfspaces: Universal Algorithms
1131. NeurIPS 2023Text-to-Image Diffusion Models are Zero Shot Classifiers
1132. NeurIPS 2023Textually Pretrained Speech Language Models
1133. NeurIPS 2023The Adversarial Consistency of Surrogate Risks for Binary Classification
1134. NeurIPS 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
1135. NeurIPS 2023The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
1136. NeurIPS 2023The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
1137. NeurIPS 2023The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
1138. NeurIPS 2023The geometry of hidden representations of large transformer models
1139. NeurIPS 2023The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
1140. NeurIPS 2023The probability flow ODE is provably fast
1141. NeurIPS 2023The Quantization Model of Neural Scaling
1142. NeurIPS 2023The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit
1143. NeurIPS 2023The Transient Nature of Emergent In-Context Learning in Transformers
1144. NeurIPS 2023Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
1145. NeurIPS 2023Theoretical and Practical Perspectives on what Influence Functions Do
1146. NeurIPS 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
1147. NeurIPS 2023Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
1148. NeurIPS 2023Toolformer: Language Models Can Teach Themselves to Use Tools
1149. NeurIPS 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
1150. NeurIPS 2023Tools for Verifying Neural Models' Training Data

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。