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 349 篇，第 2 / 7 页

51. NeurIPS 2026Constrained Look-ahead Guidance for Interference-Aware Flow Editing
52. NeurIPS 2026Contact Geometry for Generative Models: An Unbalanced Optimal Transport Formulation
53. NeurIPS 2026Continuous Personalized Diffusion Model via Spinor-Component Forward Geometry
54. NeurIPS 2026Continuously-Augmented Hybrid Masked Diffusion Model for Data Imputation
55. NeurIPS 2026Controllable Molecular Generative Foundation Models
56. NeurIPS 2026Coreset-Induced Conditional Velocity Flow Matching
57. NeurIPS 2026Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR
58. NeurIPS 2026CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
59. NeurIPS 2026Cyclic Denoising Reveals Ultrastable Memories in Diffusion Models
60. NeurIPS 2026D-DOIT: Training-free Adaptation of Discrete Diffusion via Doob's h-Transform
61. NeurIPS 2026Decomposed Graded Verifier for Generative World Modeling
62. NeurIPS 2026Decomposing Conformal Uncertainty: Calibration- and Instance-Driven Feature Attribution
63. NeurIPS 2026Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
64. NeurIPS 2026Deep Minds and Shallow Probes
65. NeurIPS 2026Denoising Time Matters: Diverse Generation in Diffusion Language Models
66. NeurIPS 2026DiffATS: Diffusion in Aligned Tensor Space
67. NeurIPS 2026Differentiable Knapsack and Top-k Operators via Dynamic Programming
68. NeurIPS 2026Diffusion Model's Generalization Can Be Characterized by Inductive Biases toward a Data-Dependent Ridge Manifold
69. NeurIPS 2026DiLaDiff: Distilled Latent-augmented Diffusion for Language Modeling
70. NeurIPS 2026Discrete Diffusion Models Exploit Asymmetry to Solve Lookahead Planning Tasks
71. NeurIPS 2026Discrete Diffusion Playground: A 2D Benchmark for Discrete Generative Models
72. NeurIPS 2026Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
73. NeurIPS 2026Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors
74. NeurIPS 2026Disentanglement as Identifiable Pushforward Factorisation
75. NeurIPS 2026Displacement Geometry Captures Platonic Shared Reality Across Models and Modalities
76. NeurIPS 2026Do Sparse Autoencoders Learn Meaningful Concept Hierarchies?
77. NeurIPS 2026dRAE: Representation Autoencoder with Hyper-Spherical Codes
78. NeurIPS 2026DrPO: Drifting Preference Optimization for One-Step Generative Models
79. NeurIPS 2026Dual-Contrastive Sparse Autoencoders Reveal Features of Musical Interpretation
80. NeurIPS 2026DualDrift: Combining Forward and Reverse Drifts for One-Step Generative Modeling
81. NeurIPS 2026DySurface: Consistent 4D Surface Reconstruction via Bridging Explicit Gaussians and Implicit Functions
82. NeurIPS 2026E-GEO: A Testbed for Generative Engine Optimization in E-Commerce
83. NeurIPS 2026Efficient Off-Policy RL for Video Generation via Forward-Consistent Reward Matching
84. NeurIPS 2026Efficient Reasoning via Constrained Optimization in Latent Space
85. NeurIPS 2026Emergence of Distortions in High-Dimensional Guided Diffusion Models
86. NeurIPS 2026Emergence, Retention and Mitigation of Ill-conditioning due to Basis Lifting in KANs
87. NeurIPS 2026Empowering Masked Diffusion Models to Self-Correct with Leave-One-Out Transformers
88. NeurIPS 2026Enabling Denoising Score Matching Type Training for Manifold Diffusion Model via Momentum and Splitting
89. NeurIPS 2026End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
90. NeurIPS 2026Energy-Tweedie: Score meets Score, Energy meets Energy
91. NeurIPS 2026Enhanced convergence guarantees of score-based generative models in $\mathcal{W}_2$-distance beyond log-concavity
92. NeurIPS 2026Entropy Distribution as a Fingerprint for Hallucinations in Generative Models
93. NeurIPS 2026Escaping the Curse of Dimensionality in One‑Step Flow-Based Generative Models
94. NeurIPS 2026Every Measurement, Every Direction, All at Once: Multimodal Flow Matching for Molecules and Spectra
95. NeurIPS 2026Exact Channel Decoupling via Joint Diagonalization and Uniform Splicing for Diffusion Transformer Quantization
96. NeurIPS 2026Expanding Flow Maps
97. NeurIPS 2026Expected Batch Optimal Transport Plans and Consequences for Flow Matching
98. NeurIPS 2026FaceProbe: Recovering HDR Environment Map via Masked Diffusion and Physical Preference
99. NeurIPS 2026Factored Generative Models through Mechanism Diversity
100. NeurIPS 2026Fast and Accurate Probing of In-Training LLMs' Downstream Performances

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。