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 12 / 16 页

551. NeurIPS 2023Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
552. NeurIPS 2023Provably (More) Sample-Efficient Offline RL with Options
553. NeurIPS 2023Provably Efficient Offline Reinforcement Learning in Regular Decision Processes
554. NeurIPS 2023PTADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios
555. NeurIPS 2023PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
556. NeurIPS 2023Punctuation-level Attack: Single-shot and Single Punctuation Can Fool Text Models
557. NeurIPS 2023QATCH: Benchmarking SQL-centric tasks with Table Representation Learning Models on Your Data
558. NeurIPS 2023Quantification of Uncertainty with Adversarial Models
559. NeurIPS 2023Quantifying the Cost of Learning in Queueing Systems
560. NeurIPS 2023QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution
561. NeurIPS 2023RADAR: Robust AI-Text Detection via Adversarial Learning
562. NeurIPS 2023Rank-N-Contrast: Learning Continuous Representations for Regression
563. NeurIPS 2023RanPAC: Random Projections and Pre-trained Models for Continual Learning
564. NeurIPS 2023Recommender Systems with Generative Retrieval
565. NeurIPS 2023REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling
566. NeurIPS 2023Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
567. NeurIPS 2023Regularization properties of adversarially-trained linear regression
568. NeurIPS 2023Regularizing Neural Networks with Meta-Learning Generative Models
569. NeurIPS 2023Rehearsal Learning for Avoiding Undesired Future
570. NeurIPS 2023ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence
571. NeurIPS 2023Reinforcement Learning with Fast and Forgetful Memory
572. NeurIPS 2023RELIC: Reproducibility and Extension on LIC metric in quantifying bias in captioning models
573. NeurIPS 2023Replicable Clustering
574. NeurIPS 2023RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
575. NeurIPS 2023Representational Strengths and Limitations of Transformers
576. NeurIPS 2023Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests
577. NeurIPS 2023Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone
578. NeurIPS 2023Resetting the Optimizer in Deep RL: An Empirical Study
579. NeurIPS 2023Residual Q-Learning: Offline and Online Policy Customization without Value
580. NeurIPS 2023ResMem: Learn what you can and memorize the rest
581. NeurIPS 2023Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning
582. NeurIPS 2023ResoNet: Noise-Trained Physics-Informed MRI Off-Resonance Correction
583. NeurIPS 2023Restart Sampling for Improving Generative Processes
584. NeurIPS 2023Rethinking Conditional Diffusion Sampling with Progressive Guidance
585. NeurIPS 2023Rethinking Gauss-Newton for learning over-parameterized models
586. NeurIPS 2023Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
587. NeurIPS 2023Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules
588. NeurIPS 2023Reverse Engineering Self-Supervised Learning
589. NeurIPS 2023Reversible and irreversible bracket-based dynamics for deep graph neural networks
590. NeurIPS 2023Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
591. NeurIPS 2023Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations
592. NeurIPS 2023Revisiting Implicit Differentiation for Learning Problems in Optimal Control
593. NeurIPS 2023Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective
594. NeurIPS 2023Revisiting the Evaluation of Image Synthesis with GANs
595. NeurIPS 2023Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View
596. NeurIPS 2023Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
597. NeurIPS 2023REx: Data-Free Residual Quantization Error Expansion
598. NeurIPS 2023RGMIL: Guide Your Multiple-Instance Learning Model with Regressor
599. NeurIPS 2023Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
600. NeurIPS 2023Robust and Actively Secure Serverless Collaborative Learning

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。