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 286 篇，第 6 / 6 页

251. ICML 2023Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
252. ICML 2023State and parameter learning with PARIS particle Gibbs
253. ICML 2023Statistical Inference and A/B Testing for First-Price Pacing Equilibria
254. ICML 2023STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
255. ICML 2023Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
256. ICML 2023Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning
257. ICML 2023Target-Aware Generative Augmentations for Single-Shot Adaptation
258. ICML 2023Test-time Adaptation with Slot-Centric Models
259. ICML 2023TGRL: An Algorithm for Teacher Guided Reinforcement Learning
260. ICML 2023The Acquisition of Physical Knowledge in Generative Neural Networks
261. ICML 2023The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
262. ICML 2023The Catalog Problem: Clustering and Ordering Variable-Sized Sets
263. ICML 2023The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
264. ICML 2023The SSL Interplay: Augmentations, Inductive Bias, and Generalization
265. ICML 2023The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
266. ICML 2023Thompson Sampling with Diffusion Generative Prior
267. ICML 2023Tighter Bounds on the Expressivity of Transformer Encoders
268. ICML 2023Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models
269. ICML 2023Towards Deep Attention in Graph Neural Networks: Problems and Remedies
270. ICML 2023Towards Sustainable Learning: Coresets for Data-efficient Deep Learning
271. ICML 2023Towards Understanding and Improving GFlowNet Training
272. ICML 2023Transformers as Algorithms: Generalization and Stability in In-context Learning
273. ICML 2023Unconstrained Online Learning with Unbounded Losses
274. ICML 2023Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective
275. ICML 2023Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits
276. ICML 2023Unlocking Slot Attention by Changing Optimal Transport Costs
277. ICML 2023Unscented Autoencoder
278. ICML 2023Unveiling the Latent Space Geometry of Push-Forward Generative Models
279. ICML 2023Variational Mixture of HyperGenerators for Learning Distributions over Functions
280. ICML 2023Vector Quantized Wasserstein Auto-Encoder
281. ICML 2023Weakly Supervised Disentangled Generative Causal Representation Learning
282. ICML 2023When is Realizability Sufficient for Off-Policy Reinforcement Learning?
283. ICML 2023When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
284. ICML 2023Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
285. ICML 2023Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?
286. ICML 2023Width and Depth Limits Commute in Residual Networks

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。