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 776 篇，第 6 / 16 页

251. NeurIPS 2023Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events
252. NeurIPS 2023Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
253. NeurIPS 2023EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
254. NeurIPS 2023Expert load matters: operating networks at high accuracy and low manual effort
255. NeurIPS 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
256. NeurIPS 2023Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
257. NeurIPS 2023Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
258. NeurIPS 2023Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
259. NeurIPS 2023Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
260. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning
261. NeurIPS 2023FABind: Fast and Accurate Protein-Ligand Binding
262. NeurIPS 2023Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
263. NeurIPS 2023FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
264. NeurIPS 2023Facing Off World Model Backbones: RNNs, Transformers, and S4
265. NeurIPS 2023Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
266. NeurIPS 2023Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
267. NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
268. NeurIPS 2023Fast Attention Requires Bounded Entries
269. NeurIPS 2023Fast Optimal Locally Private Mean Estimation via Random Projections
270. NeurIPS 2023Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees
271. NeurIPS 2023Fast Trainable Projection for Robust Fine-tuning
272. NeurIPS 2023Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
273. NeurIPS 2023Feature Learning for Interpretable, Performant Decision Trees
274. NeurIPS 2023Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
275. NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
276. NeurIPS 2023Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
277. NeurIPS 2023Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds
278. NeurIPS 2023Federated Learning with Manifold Regularization and Normalized Update Reaggregation
279. NeurIPS 2023Federated Spectral Clustering via Secure Similarity Reconstruction
280. NeurIPS 2023Fine-Tuning Language Models with Just Forward Passes
281. NeurIPS 2023FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
282. NeurIPS 2023First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
283. NeurIPS 2023Flow Factorized Representation Learning
284. NeurIPS 2023Flow: Per-instance Personalized Federated Learning
285. NeurIPS 2023Formulating Discrete Probability Flow Through Optimal Transport
286. NeurIPS 2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
287. NeurIPS 2023Full-Atom Protein Pocket Design via Iterative Refinement
288. NeurIPS 2023Functional Renyi Differential Privacy for Generative Modeling
289. NeurIPS 2023Fundamental Limits and Tradeoffs in Invariant Representation Learning
290. NeurIPS 2023Game Solving with Online Fine-Tuning
291. NeurIPS 2023Gaussian Mixture Solvers for Diffusion Models
292. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
293. NeurIPS 2023Generalized Information-theoretic Multi-view Clustering
294. NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
295. NeurIPS 2023Generalizing Nonlinear ICA Beyond Structural Sparsity
296. NeurIPS 2023Generating QM1B with PySCF$_{\text{IPU}}$
297. NeurIPS 2023Generative Category-level Object Pose Estimation via Diffusion Models
298. NeurIPS 2023Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
299. NeurIPS 2023Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
300. NeurIPS 2023Generative Neural Fields by Mixtures of Neural Implicit Functions

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。