Deep learning · generative models and autoencoders papers, 2023–2026

Deep learning area: generative_models_and_autoencoders

Scope: NeurIPS, ICLR, ICML and CVPR, 2023–2026. 4,314 papers are tagged with this topic.

Key findings

Papers per year

Papers per venue

Counts by year and venue

Deep learning · generative models and autoencoders papers by year and venue
YearNeurIPSICLRICMLCVPRTotal
202377678286761,216
2024349174139106768
2025240275275110900
20263493036041741,430

Representative papers

Spread across years and venues so every edition shows up

All Deep learning · generative models and autoencoders papers

900 papers, page 15 of 18

  1. NeurIPS 2025Continuity and Isolation Lead to Doubts or Dilemmas in Large Language Models
  2. NeurIPS 2025Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
  3. NeurIPS 2025Controllable Human-centric Keyframe Interpolation with Generative Prior
  4. NeurIPS 2025CORAL: Disentangling Latent Representations in Long-Tailed Diffusion
  5. NeurIPS 2025Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning
  6. NeurIPS 2025Counterfactual Identifiability via Dynamic Optimal Transport
  7. NeurIPS 2025Coupling Generative Modeling and an Autoencoder with the Causal Bridge
  8. NeurIPS 2025Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training
  9. NeurIPS 2025DeblurDiff: Real-Word Image Deblurring with Generative Diffusion Models
  10. NeurIPS 2025DeCaFlow: A deconfounding causal generative model
  11. NeurIPS 2025Detecting Generated Images by Fitting Natural Image Distributions
  12. NeurIPS 2025Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior
  13. NeurIPS 2025Diffusion Beats Autoregressive in Data-Constrained Settings
  14. NeurIPS 2025Diffusion Generative Modeling on Lie Group Representations
  15. NeurIPS 2025Diffusion on Demand: Selective Caching and Modulation for Efficient Generation
  16. NeurIPS 2025Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification
  17. NeurIPS 2025Dimension-free Score Matching and Time Bootstrapping for Diffusion Models
  18. NeurIPS 2025Dimensional Collapse in VQVAEs: Evidence and Remedies
  19. NeurIPS 2025Distributional Autoencoders Know the Score
  20. NeurIPS 2025Dynamic and Chemical Constraints to Enhance the Molecular Masked Graph Autoencoders
  21. NeurIPS 2025Efficient Algorithms for Robust and Partial Semi-Discrete Optimal Transport
  22. NeurIPS 2025Efficient Multimodal Dataset Distillation via Generative Models
  23. NeurIPS 2025Emergence and Evolution of Interpretable Concepts in Diffusion Models
  24. NeurIPS 2025Encoder-Decoder Diffusion Language Models for Efficient Training and Inference
  25. NeurIPS 2025Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
  26. NeurIPS 2025Entropic Time Schedulers for Generative Diffusion Models
  27. NeurIPS 2025Entropy Rectifying Guidance for Diffusion and Flow Models
  28. NeurIPS 2025Exponential Convergence Guarantees for Iterative Markovian Fitting
  29. NeurIPS 2025Fine-grained List-wise Alignment for Generative Medication Recommendation
  30. NeurIPS 2025Flash Invariant Point Attention
  31. NeurIPS 2025Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning
  32. NeurIPS 2025FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
  33. NeurIPS 2025FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion
  34. NeurIPS 2025Fractional Diffusion Bridge Models
  35. NeurIPS 2025From Information to Generative Exponent: Learning Rate Induces Phase Transitions in SGD
  36. NeurIPS 2025From Likelihood to Fitness: Improving Variant Effect Prediction in Protein and Genome Language Models
  37. NeurIPS 2025FuncGenFoil: Airfoil Generation and Editing Model in Function Space
  38. NeurIPS 2025GenColor: Generative and Expressive Color Enhancement with Pixel-Perfect Texture Preservation
  39. NeurIPS 2025GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
  40. NeurIPS 2025Generating Creative Chess Puzzles
  41. NeurIPS 2025Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations
  42. NeurIPS 2025Generating Physically Sound Designs from Text and a Set of Physical Constraints
  43. NeurIPS 2025Generation as Search Operator for Test-Time Scaling of Diffusion-based Combinatorial Optimization
  44. NeurIPS 2025Generative Caching for Structurally Similar Prompts and Responses
  45. NeurIPS 2025Generative Data Augmentation via Diffusion Distillation, Adversarial Alignment, and Importance Reweighting
  46. NeurIPS 2025Generative diffusion for perceptron problems: statistical physics analysis and efficient algorithms
  47. NeurIPS 2025Generative Distribution Embeddings
  48. NeurIPS 2025Generative Graph Pattern Machine
  49. NeurIPS 2025Generative Model Inversion Through the Lens of the Manifold Hypothesis
  50. NeurIPS 2025Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings

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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.

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