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

1,304 papers, page 1 of 27

1. ICML 2026(1D) Ordered Tokens Enable Efficient Test-Time Search
2. ICML 2026$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
3. ICML 20263D MeanFlow: One-Step Point Cloud Completion and Generation via Average-Velocity Transport
4. ICML 2026A Diffusive Classification Loss for Learning Energy-based Generative Models
5. ICML 2026A Flat Vocabulary or a Rich Hierarchy? Re-introducing Intrinsic Structure Transforms the Autoregressive Image Generation
6. ICML 2026A Kinetic Energy Perspective of Flow Matching
7. ICML 2026A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies
8. ICML 2026A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms
9. ICML 2026A Solvable High-Dimensional Model Where Nonlinear Autoencoders Learn Structure Invisible to PCA While Test Loss Misaligns With Generalization
10. ICML 2026A Theoretical Framework for Statistical Evaluability of Generative Models
11. ICML 2026A Time-Reparameterized Cumulative Intensity Extrapolation Sampler for Discrete Flow Matching
12. ICML 2026A Unifying View of Variational Generative Wasserstein Flows
13. ICML 2026AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation
14. ICML 2026Adapting Noise to Data: Generative Flows from Learned 1D Processes
15. ICML 2026Adversarial Flow Models
16. ICML 2026Adversarial Latent Embedding Repair for LLM Continual Learning
17. ICML 2026Align Your Trajectory Tangent: Training Better Consistency Models via Manifold-Aligned Tangents
18. ICML 2026Alignment-Guided Score Matching for Text-to-Image Alignment in Diffusion Models
19. ICML 2026Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training
20. ICML 2026Ambient Dataloops: Generative Models for Dataset Refinement
21. ICML 2026Amortized Maximum Inner Product Search with Learned Support Functions
22. ICML 2026An Interactive Paradigm for Deep Research
23. ICML 2026Analytic Bijections for Smooth and Interpretable Normalizing Flows
24. ICML 2026Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture
25. ICML 2026AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors
26. ICML 2026Anytime Safe PAC Efficient Reasoning
27. ICML 2026ARC-Decode: Accelerated Decoding with Risk-Bounded Acceptance
28. ICML 2026ArcDAE: Asymmetric Rectified Contrastive Diffusion Autoencoder for Unified Representation Learning
29. ICML 2026Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach
30. ICML 2026AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning
31. ICML 2026ASIR: Steganography for Diffusion Models via Antipodal Sampling and Iterative Recovery
32. ICML 2026At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization
33. ICML 2026Attacks on Machine-Text Detectors Retain Stylistic Fingerprints
34. ICML 2026Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
35. ICML 2026Attention Sparsity is Input-Stable: Training-Free Sparse Attention for Video Generation via Offline Sparsity Profiling and Online QK Co-Clustering
36. ICML 2026Automatic Layer Selection for Hallucination Detection
37. ICML 2026Autoregression with Self-Token Prediction
38. ICML 2026Autoregressive Image Generation with Masked Bit Modeling
39. ICML 2026AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents
40. ICML 2026Balancing Plasticity and Stability with Fast and Slow Successor Features
41. ICML 2026Base Models Know How to Reason, Thinking Models Learn When
42. ICML 2026Behavioral Mode Discovery for Fine-tuning Multimodal Generative Policies
43. ICML 2026Being More Lightweight and Practical: Mini-sized Contrastive Learning Pre-trained Models for Fine-grained Traffic Task
44. ICML 2026Benchmarking and Improving Fine-Grained Text-to-Image Alignment via Paired Reinforcement Learning
45. ICML 2026Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching
46. ICML 2026Beyond Generative Priors: Minority Sampling with JEPA-Guided Diffusion
47. ICML 2026Beyond Looking Up, Try Looking Around: Harmonizing Global Structure and Local Consistency in Optimal Transport for Short Text Clustering
48. ICML 2026Beyond Pixel Histories: World Models with Persistent 3D State
49. ICML 2026Beyond Reactivity: Proactive Adaptive Conformal Inference for Online LLM Factuality
50. ICML 2026Bi-Anchor Interpolation Solver for Accelerating Generative Modeling

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.