Deep learning · generative models and autoencoders papers, 2023–2026

Key findings

• Deep learning · generative models and autoencoders went from 1,216 papers in 2023 to 1,430 in 2026, roughly 1.2x in three years.
• NeurIPS publishes the most Deep learning · generative models and autoencoders work: 1,714 papers from 2023 to 2026, 40% of the four-venue total.
• The fastest-growing overlap is with Image Generation: papers tagged with both rose from 35 in 2023 to 150 in 2026.
• Among the 11 topics under Deep Learning, it ranks #2 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 1.2x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Deep learning · generative models and autoencoders papers

139 papers, page 2 of 3

51. ICML 2024GiLOT: Interpreting Generative Language Models via Optimal Transport
52. ICML 2024Graph Geometry-Preserving Autoencoders
53. ICML 2024Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
54. ICML 2024HexGen: Generative Inference of Large Language Model over Heterogeneous Environment
55. ICML 2024High-Performance Temporal Reversible Spiking Neural Networks with $\mathcal{O}(L)$ Training Memory and $\mathcal{O}(1)$ Inference Cost
56. ICML 2024How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
57. ICML 2024How to Trace Latent Generative Model Generated Images without Artificial Watermark?
58. ICML 2024Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
59. ICML 2024Image Hijacks: Adversarial Images can Control Generative Models at Runtime
60. ICML 2024Imitation Learning from Purified Demonstrations
61. ICML 2024Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining
62. ICML 2024Indirectly Parameterized Concrete Autoencoders
63. ICML 2024Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
64. ICML 2024Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
65. ICML 2024Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations
66. ICML 2024Language Models as Semantic Indexers
67. ICML 2024LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging
68. ICML 2024Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders
69. ICML 2024Learning to Infer Generative Template Programs for Visual Concepts
70. ICML 2024Learning to Model the World With Language
71. ICML 2024Learning Useful Representations of Recurrent Neural Network Weight Matrices
72. ICML 2024Light and Optimal Schrödinger Bridge Matching
73. ICML 2024Listening to the noise: Blind Denoising with Gibbs Diffusion
74. ICML 2024Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining
75. ICML 2024Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
76. ICML 2024Make-A-Shape: a Ten-Million-scale 3D Shape Model
77. ICML 2024Masked Face Recognition with Generative-to-Discriminative Representations
78. ICML 2024Mean-field Chaos Diffusion Models
79. ICML 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
80. ICML 2024Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems
81. ICML 2024Nearest Neighbour Score Estimators for Diffusion Generative Models
82. ICML 2024Neural Diffusion Models
83. ICML 2024NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction
84. ICML 2024ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
85. ICML 2024On Mechanistic Knowledge Localization in Text-to-Image Generative Models
86. ICML 2024On the Role of Edge Dependency in Graph Generative Models
87. ICML 2024OxyGenerator: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning
88. ICML 2024Partial Optimality in the Linear Ordering Problem
89. ICML 2024Particle Denoising Diffusion Sampler
90. ICML 2024PcLast: Discovering Plannable Continuous Latent States
91. ICML 2024Position: A Safe Harbor for AI Evaluation and Red Teaming
92. ICML 2024Position: Compositional Generative Modeling: A Single Model is Not All You Need
93. ICML 2024Position: Data-driven Discovery with Large Generative Models
94. ICML 2024Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation
95. ICML 2024Position: Explain to Question not to Justify
96. ICML 2024Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI
97. ICML 2024Positive and Unlabeled Learning with Controlled Probability Boundary Fence
98. ICML 2024Preventing Model Collapse in Gaussian Process Latent Variable Models
99. ICML 2024Pricing with Contextual Elasticity and Heteroscedastic Valuation
100. ICML 2024Projecting Molecules into Synthesizable Chemical Spaces

FAQ

How many Deep learning · generative models and autoencoders papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

4,314 in total: 1,216 in 2023, 768 in 2024, 900 in 2025, 1,430 in 2026.

How many Deep learning · generative models and autoencoders papers are at NeurIPS 2026?

NeurIPS 2026 has 349 Deep learning · generative models and autoencoders papers, versus 240 the year before.

How many Deep learning · generative models and autoencoders papers are at ICLR 2026?

ICLR 2026 has 303 Deep learning · generative models and autoencoders papers, versus 275 the year before.

How many Deep learning · generative models and autoencoders papers are at ICML 2026?

ICML 2026 has 604 Deep learning · generative models and autoencoders papers, versus 275 the year before.

How many Deep learning · generative models and autoencoders papers are at CVPR 2026?

CVPR 2026 has 174 Deep learning · generative models and autoencoders papers, versus 110 the year before.

Which conference publishes the most Deep learning · generative models and autoencoders papers?

NeurIPS, with 1,714 papers from 2023 to 2026.