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 174 篇，第 4 / 4 页

151. ICLR 2024Single Motion Diffusion
152. ICLR 2024SliceGPT: Compress Large Language Models by Deleting Rows and Columns
153. ICLR 2024Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community
154. ICLR 2024Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models
155. ICLR 2024Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
156. ICLR 2024Sparse Autoencoders Find Highly Interpretable Features in Language Models
157. ICLR 2024STREAM: Spatio-TempoRal Evaluation and Analysis Metric for Video Generative Models
158. ICLR 2024Talk like a Graph: Encoding Graphs for Large Language Models
159. ICLR 2024TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
160. ICLR 2024The Generative AI Paradox: “What It Can Create, It May Not Understand”
161. ICLR 2024Towards Generative Abstract Reasoning: Completing Raven’s Progressive Matrix via Rule Abstraction and Selection
162. ICLR 2024Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
163. ICLR 2024Towards Non-Asymptotic Convergence for Diffusion-Based Generative Models
164. ICLR 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
165. ICLR 2024Transformer-VQ: Linear-Time Transformers via Vector Quantization
166. ICLR 2024TUVF: Learning Generalizable Texture UV Radiance Fields
167. ICLR 2024Unbiased Watermark for Large Language Models
168. ICLR 2024Understanding the Effects of RLHF on LLM Generalisation and Diversity
169. ICLR 2024Universal Guidance for Diffusion Models
170. ICLR 2024Unveiling and Manipulating Prompt Influence in Large Language Models
171. ICLR 2024Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis
172. ICLR 2024Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency
173. ICLR 2024WizardCoder: Empowering Code Large Language Models with Evol-Instruct
174. ICLR 2024WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions

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。