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

604 papers, page 2 of 13

51. ICML 2026Biased Generalization in Diffusion Models
52. ICML 2026Boosting World Models Learning via Latent-Space Value Alignment
53. ICML 2026Breaking the Factorization Barrier in Diffusion Language Models
54. ICML 2026Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation
55. ICML 2026Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
56. ICML 2026Breaking the Synthetic-Real Domain Shortcut for Training-Free Generative Replay-based Class Incremental Learning
57. ICML 2026BRIDGE: Predicting Human Task Completion Time From Model Performance
58. ICML 2026Bridging Functional and Representational Similarity via Usable Information
59. ICML 2026Budget-Constrained Step-Level Diffusion Caching
60. ICML 2026C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders
61. ICML 2026CacheEdit: Efficient Multi-round Image Editing via Adaptive Token-wise Reuse.
62. ICML 2026Calibrated Test-Time Guidance for Bayesian Inference
63. ICML 2026Calibrating Generative Models to Distributional Constraints
64. ICML 2026CameraNoise: Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping
65. ICML 2026CamGeo: Sparse Camera-Conditioned Image-to-Video Generation with 3D Geometry Priors
66. ICML 2026CANDI: Hybrid Discrete-Continuous Diffusion Models
67. ICML 2026Capability Traps in DPO
68. ICML 2026CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation
69. ICML 2026Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features
70. ICML 2026Categorical Flow Maps
71. ICML 2026Categorical Reparameterization with Denoising Diffusion Models
72. ICML 2026CatFlow: Co-generation of Slab-Adsorbate Systems via Flow Matching
73. ICML 2026Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation
74. ICML 2026Causal Representation Learning with Optimal Compression and Complex Treatments
75. ICML 2026Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
76. ICML 2026CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models
77. ICML 2026Co-Generative De Novo Functional Protein Design
78. ICML 2026CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation
79. ICML 2026CoFrGeNet: Continued Fraction Architectures for Language Generation
80. ICML 2026CoGenCast: A Coupled Autoregressive–Flow Generative Framework for Time Series Forecasting
81. ICML 2026CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning
82. ICML 2026ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox
83. ICML 2026Compositional Generative Modeling from Decentralized Data
84. ICML 2026Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models
85. ICML 2026Computational Arbitrage in AI Model Markets
86. ICML 2026Compute Where it Counts: Self Optimizing Language Models
87. ICML 2026Concept Removal for Frontier Image Generative Models
88. ICML 2026Concept-Guided Tokenization: Closing the Gap Between Reconstruction and Generation
89. ICML 2026Condition-Aware Graph Flow Matching for Modeling the Distributions of Complex Fluid Systems
90. ICML 2026Conf-Gen: Conformal Uncertainty Quantification for Generative Models
91. ICML 2026Confidence is Not Universal: Task-Dependent Calibration and Emergent Behavior in LLMs
92. ICML 2026Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
93. ICML 2026Conformal C2ST: Turning weak classifiers into strong two-sample tests
94. ICML 2026Consistent Diffusion Language Models
95. ICML 2026Content-Style Identification via Differential Independence
96. ICML 2026Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation
97. ICML 2026Contrastive Flow Map Matching
98. ICML 2026ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
99. ICML 2026Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steering
100. ICML 2026CountsDiff: A diffusion model on the natural numbers for generation and imputation of count-based data

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.