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

240 papers, page 2 of 5

51. NeurIPS 2025Detecting Generated Images by Fitting Natural Image Distributions
52. NeurIPS 2025Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior
53. NeurIPS 2025Diffusion Beats Autoregressive in Data-Constrained Settings
54. NeurIPS 2025Diffusion Generative Modeling on Lie Group Representations
55. NeurIPS 2025Diffusion on Demand: Selective Caching and Modulation for Efficient Generation
56. NeurIPS 2025Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification
57. NeurIPS 2025Dimension-free Score Matching and Time Bootstrapping for Diffusion Models
58. NeurIPS 2025Dimensional Collapse in VQVAEs: Evidence and Remedies
59. NeurIPS 2025Distributional Autoencoders Know the Score
60. NeurIPS 2025Dynamic and Chemical Constraints to Enhance the Molecular Masked Graph Autoencoders
61. NeurIPS 2025Efficient Algorithms for Robust and Partial Semi-Discrete Optimal Transport
62. NeurIPS 2025Efficient Multimodal Dataset Distillation via Generative Models
63. NeurIPS 2025Emergence and Evolution of Interpretable Concepts in Diffusion Models
64. NeurIPS 2025Encoder-Decoder Diffusion Language Models for Efficient Training and Inference
65. NeurIPS 2025Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
66. NeurIPS 2025Entropic Time Schedulers for Generative Diffusion Models
67. NeurIPS 2025Entropy Rectifying Guidance for Diffusion and Flow Models
68. NeurIPS 2025Exponential Convergence Guarantees for Iterative Markovian Fitting
69. NeurIPS 2025Fine-grained List-wise Alignment for Generative Medication Recommendation
70. NeurIPS 2025Flash Invariant Point Attention
71. NeurIPS 2025Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning
72. NeurIPS 2025FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
73. NeurIPS 2025FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion
74. NeurIPS 2025Fractional Diffusion Bridge Models
75. NeurIPS 2025From Information to Generative Exponent: Learning Rate Induces Phase Transitions in SGD
76. NeurIPS 2025From Likelihood to Fitness: Improving Variant Effect Prediction in Protein and Genome Language Models
77. NeurIPS 2025FuncGenFoil: Airfoil Generation and Editing Model in Function Space
78. NeurIPS 2025GenColor: Generative and Expressive Color Enhancement with Pixel-Perfect Texture Preservation
79. NeurIPS 2025GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
80. NeurIPS 2025Generating Creative Chess Puzzles
81. NeurIPS 2025Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations
82. NeurIPS 2025Generating Physically Sound Designs from Text and a Set of Physical Constraints
83. NeurIPS 2025Generation as Search Operator for Test-Time Scaling of Diffusion-based Combinatorial Optimization
84. NeurIPS 2025Generative Caching for Structurally Similar Prompts and Responses
85. NeurIPS 2025Generative Data Augmentation via Diffusion Distillation, Adversarial Alignment, and Importance Reweighting
86. NeurIPS 2025Generative diffusion for perceptron problems: statistical physics analysis and efficient algorithms
87. NeurIPS 2025Generative Distribution Embeddings
88. NeurIPS 2025Generative Graph Pattern Machine
89. NeurIPS 2025Generative Model Inversion Through the Lens of the Manifold Hypothesis
90. NeurIPS 2025Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings
91. NeurIPS 2025Generative Perception of Shape and Material from Differential Motion
92. NeurIPS 2025Generative Pre-trained Autoregressive Diffusion Transformer
93. NeurIPS 2025Generative Trajectory Stitching through Diffusion Composition
94. NeurIPS 2025GenIR: Generative Visual Feedback for Mental Image Retrieval
95. NeurIPS 2025GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning
96. NeurIPS 2025Gradient Variance Reveals Failure Modes in Flow-Based Generative Models
97. NeurIPS 2025Graph Diffusion that can Insert and Delete
98. NeurIPS 2025GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfer
99. NeurIPS 2025High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction
100. NeurIPS 2025High-Order Flow Matching: Unified Framework and Sharp Statistical Rates

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.