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

286 papers, page 1 of 6

1. ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
2. ICML 2023A Flexible Diffusion Model
3. ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
4. ICML 2023A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
5. ICML 2023A Mathematical Model for Curriculum Learning for Parities
6. ICML 2023A Modern Look at the Relationship between Sharpness and Generalization
7. ICML 2023A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
8. ICML 2023A Statistical Perspective on Retrieval-Based Models
9. ICML 2023A theory of continuous generative flow networks
10. ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
11. ICML 2023abess: A Fast Best-Subset Selection Library in Python and R
12. ICML 2023Action Matching: Learning Stochastic Dynamics from Samples
13. ICML 2023AdaBoost is not an Optimal Weak to Strong Learner
14. ICML 2023AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
15. ICML 2023Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
16. ICML 2023Adaptive IMLE for Few-shot Pretraining-free Generative Modelling
17. ICML 2023Adversarial Cheap Talk
18. ICML 2023Adversarial Learning of Distributional Reinforcement Learning
19. ICML 2023An SDE for Modeling SAM: Theory and Insights
20. ICML 2023Analyzing Diffusion as Serial Reproduction
21. ICML 2023Attributing Image Generative Models using Latent Fingerprints
22. ICML 2023Bayes-optimal Learning of Deep Random Networks of Extensive-width
23. ICML 2023Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts
24. ICML 2023Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
25. ICML 2023Better Diffusion Models Further Improve Adversarial Training
26. ICML 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectories
27. ICML 2023BiBench: Benchmarking and Analyzing Network Binarization
28. ICML 2023Bidirectional Learning for Offline Model-based Biological Sequence Design
29. ICML 2023Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers
30. ICML 2023Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
31. ICML 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
32. ICML 2023Brauer's Group Equivariant Neural Networks
33. ICML 2023Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
34. ICML 2023Calibrating Multimodal Learning
35. ICML 2023CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data
36. ICML 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
37. ICML 2023Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
38. ICML 2023CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
39. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
40. ICML 2023Composer: Creative and Controllable Image Synthesis with Composable Conditions
41. ICML 2023Compositional Exemplars for In-context Learning
42. ICML 2023Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
43. ICML 2023Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
44. ICML 2023ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
45. ICML 2023Conditionally Strongly Log-Concave Generative Models
46. ICML 2023Conditions and Assumptions for Constraint-based Causal Structure Learning
47. ICML 2023Consistency Models
48. ICML 2023Continuation Path Learning for Homotopy Optimization
49. ICML 2023ContraBAR: Contrastive Bayes-Adaptive Deep RL
50. ICML 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining

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.