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

776 papers, page 1 of 16

1. NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
2. NeurIPS 2023[Re] Masked Autoencoders Are Small Scale Vision Learners: A Reproduction Under Resource Constraints
3. NeurIPS 2023[Re] VAE Approximation Error: ELBO and Exponential Families
4. NeurIPS 2023$\mathcal{M}^4$: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models
5. NeurIPS 2023$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning
6. NeurIPS 2023$L_2$-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting
7. NeurIPS 2023$p$-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison
8. NeurIPS 2023$S^3$: Increasing GPU Utilization during Generative Inference for Higher Throughput
9. NeurIPS 2023$SE(3)$ Equivariant Convolution and Transformer in Ray Space
10. NeurIPS 20233D-Aware Visual Question Answering about Parts, Poses and Occlusions
11. NeurIPS 20234M: Massively Multimodal Masked Modeling
12. NeurIPS 2023A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
13. NeurIPS 2023A Bayesian Take on Gaussian Process Networks
14. NeurIPS 2023A Competitive Algorithm for Agnostic Active Learning
15. NeurIPS 2023A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
16. NeurIPS 2023A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
17. NeurIPS 2023A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains
18. NeurIPS 2023A fast heuristic to optimize time-space tradeoff for large models
19. NeurIPS 2023A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
20. NeurIPS 2023A General Theory of Correct, Incorrect, and Extrinsic Equivariance
21. NeurIPS 2023A generative model of the hippocampal formation trained with theta driven local learning rules
22. NeurIPS 2023A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space
23. NeurIPS 2023A Long $N$-step Surrogate Stage Reward for Deep Reinforcement Learning
24. NeurIPS 2023A new perspective on building efficient and expressive 3D equivariant graph neural networks
25. NeurIPS 2023A Path to Simpler Models Starts With Noise
26. NeurIPS 2023A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints
27. NeurIPS 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
28. NeurIPS 2023A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories
29. NeurIPS 2023A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm
30. NeurIPS 2023A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications
31. NeurIPS 2023A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm
32. NeurIPS 2023A Unified Framework for U-Net Design and Analysis
33. NeurIPS 2023A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing
34. NeurIPS 2023A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning
35. NeurIPS 2023A Unified Model and Dimension for Interactive Estimation
36. NeurIPS 2023A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning
37. NeurIPS 2023A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
38. NeurIPS 2023Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation
39. NeurIPS 2023Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement
40. NeurIPS 2023Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
41. NeurIPS 2023Active Negative Loss Functions for Learning with Noisy Labels
42. NeurIPS 2023Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects
43. NeurIPS 2023Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
44. NeurIPS 2023AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders
45. NeurIPS 2023Add and Thin: Diffusion for Temporal Point Processes
46. NeurIPS 2023Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
47. NeurIPS 2023Addressing Negative Transfer in Diffusion Models
48. NeurIPS 2023ADGym: Design Choices for Deep Anomaly Detection
49. NeurIPS 2023Adversarial Examples Are Not Real Features
50. NeurIPS 2023Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation

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.