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

349 papers, page 1 of 7

1. NeurIPS 2026$\mathcal{P}$Torch: Narrowing the Gap Between Projection and Gradient-Based Learning
2. NeurIPS 2026A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs
3. NeurIPS 2026A Locally Tokenized Generative Model for Robust Time-Series Watermarking
4. NeurIPS 2026A Mean-Field Framework for Inference-Time Distributional Control of Diffusion Models
5. NeurIPS 2026A simple model of co-emergence of grid and place fields
6. NeurIPS 2026A Unifying Perspective on Language Model Interpretability
7. NeurIPS 2026ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
8. NeurIPS 2026Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
9. NeurIPS 2026Active Learning for Conditional Generative Compressed Sensing
10. NeurIPS 2026Active Learning of Conditional Generative Models via the Transport Neural Tangent Kernel
11. NeurIPS 2026Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
12. NeurIPS 2026AegisFlow: Training-free Non-myopic Path-safe Guided Flow Matching
13. NeurIPS 2026AesGI-Bench: Benchmarking and Evaluating the Aesthetic Quality of AI-Generated Images via Large Multimodal Models
14. NeurIPS 2026AI-Assisted Classification under Correlation Neglect and Trust
15. NeurIPS 2026Align $\&$ Invert: Solving Inverse Problems with Diffusion and Flow-based Models via Representation Alignment
16. NeurIPS 2026Align as You Couple: Learning Spatial Resolved Inference from H&E Images with Mollified Flow Matching
17. NeurIPS 2026Aligning Flow Map Policies with Optimal $Q$-Guidance
18. NeurIPS 2026All-Addition Spiking Diffusion Models with Attention Enhancement
19. NeurIPS 2026AMGenC: Generating Charge Balanced Amorphous Materials
20. NeurIPS 2026Analyzing Learning-Dynamics Across Loss and Prediction Choices for Diffusion Models
21. NeurIPS 2026Architecture-Embedded Physics Priors for Mitigating Spectral Bias in Physics-Informed Neural Networks
22. NeurIPS 2026Are we really tilting? The mechanics of reward guidance in flow and diffusion models
23. NeurIPS 2026Auto-Rubric as Reward: From Implicit Preference to Explicit Generative Criteria
24. NeurIPS 2026Behaving Better, Thinking Worse: Sycophancy Across Post-Training Stages
25. NeurIPS 2026Bernoulli Flow Models: Self-Consistent Generative Modeling for Binary Data
26. NeurIPS 2026Beyond ICA: Identifiability by Symmetry Breaking
27. NeurIPS 2026Beyond Interpretability: When, Why, and How Sparse Autoencoders Enable Label-Free Visual Steering
28. NeurIPS 2026Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction
29. NeurIPS 2026Beyond Success Rates: Trainability and Extractability in Offline GCRL
30. NeurIPS 2026BitDance: Scaling Autoregressive Generative Models with Binary Tokens
31. NeurIPS 2026Black-box model classification under the discriminative factorization
32. NeurIPS 2026Bottleneck Benchmark: Sequence Compression Under Controlled Difficulty and Fixed Latent Size
33. NeurIPS 2026Boule or Baguette? A Study on Task Topology, Length Generalization, and the Benefit of Reasoning Trace
34. NeurIPS 2026BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics
35. NeurIPS 2026Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions
36. NeurIPS 2026Bridge Graphical Models: Coupling, Projection, and Current-Preserving Dynamics for Generative Modeling
37. NeurIPS 2026Bypassing PC1 Makes SAEs More Reproducible
38. NeurIPS 2026Calibrating Generative Models to Feature Distributions with MMD Finetuning
39. NeurIPS 2026Causal Bias Detection in Generative Artifical Intelligence
40. NeurIPS 2026Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
41. NeurIPS 2026Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machine
42. NeurIPS 2026ChiP-STAR: Spatial-Topological Attention for Pre-trained Generative Chip Routing
43. NeurIPS 2026Classification at the Edge of Stability: Unifying Self-Stabilization and Convergence Rates
44. NeurIPS 2026Closed-Loop Alignment: Socially Coupled In-Context Learning and Relational Posterior Collapse
45. NeurIPS 2026Coarse-to-Real: Generative Rendering for Populated Dynamic Scenes
46. NeurIPS 2026Collapse Hunter: Tackling the Dimensional Degeneration in Generative Ranking
47. NeurIPS 2026Comparing Transformers and Hybrid Models at the Token Level
48. NeurIPS 2026Concept frustration: Aligning human concepts and machine representations
49. NeurIPS 2026Conformal Cache: Reliable Proxy-Discrepancy Caching for Fast Generative Inference
50. NeurIPS 2026Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves

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.