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

1,304 papers, page 9 of 27

  1. ICML 2026PromptRL: Prompt Matters in RL for Flow-Based Image Generation
  2. ICML 2026Protein Fold Classification at Scale: Benchmarking and Pretraining
  3. ICML 2026Prototype-Grounded Concept Models for Verifiable Concept Alignment
  4. ICML 2026ProtoVAR: Efficient Dataset Distillation via Prototype-Guided Visual Autoregressive Modeling
  5. ICML 2026Proximal-Based Generative Modeling for Bayesian Inverse Problems
  6. ICML 2026Pull Requests as a Training Signal for Repo-Level Code Editing
  7. ICML 2026PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
  8. ICML 2026Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
  9. ICML 2026Q-Tab: Quantized Tabular Data Generator
  10. ICML 2026Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization
  11. ICML 2026Quantifying and Optimizing Simplicity via Polynomial Representations
  12. ICML 2026Quantifying Error Propagation and Model Collapse in Diffusion Models
  13. ICML 2026Quantum latent distributions in deep generative models
  14. ICML 2026Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects
  15. ICML 2026RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry
  16. ICML 2026RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
  17. ICML 2026RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization
  18. ICML 2026Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
  19. ICML 2026Rare Event Analysis of Large Language Models
  20. ICML 2026Rationality Measurement and Theory for Reinforcement Learning Agents
  21. ICML 2026ReaForest: Fostering Generative Video Reasoning for Spatial Planning
  22. ICML 2026Reasoning on the Manifold: Bidirectional Consistency for Self-Verification in Diffusion Language Models
  23. ICML 2026Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
  24. ICML 2026Reconstructing Template-Memorized Images from Natural Prompts
  25. ICML 2026Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
  26. ICML 2026ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
  27. ICML 2026Reinforcement Learning with Verifiable Rewards: GRPO's Loss, Dynamics, and Success Amplification
  28. ICML 2026RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse
  29. ICML 2026Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics
  30. ICML 2026Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining
  31. ICML 2026Rényi Diffusion Models
  32. ICML 2026RePack then Refine: Efficient Diffusion Transformers with Vision Foundation Models
  33. ICML 2026Responsible Text-to-Image Diffusion: Interpretable and Linearly Controllable Semantics for Fair and Safe Generation
  34. ICML 2026Restoring Initial Noise Sensitivity in Text-to-Image Distillation through Geometric Alignment
  35. ICML 2026ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
  36. ICML 2026Rethinking 3D Shape Generation: Diffusion over Superquadrics
  37. ICML 2026Rethinking Generative Image Pretraining: How Far Are We From Scaling Up Next-Pixel Prediction?
  38. ICML 2026Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
  39. ICML 2026Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
  40. ICML 2026Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance
  41. ICML 2026Rethinking Visual Intelligence: Insights from Video Pretraining
  42. ICML 2026RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
  43. ICML 2026Revisiting Spectral Representations in Generative Diffusion Models
  44. ICML 2026RGGT: A Generative-Prior-Guided Transformer for Unified Rigid and Non-Rigid Point Cloud Registration
  45. ICML 2026Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
  46. ICML 2026Riemannian MeanFlow
  47. ICML 2026Riemannian MeanFlow for One-Step Generation on Manifolds
  48. ICML 2026Riemannian Neural Optimal Transport
  49. ICML 2026RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
  50. ICML 2026RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization

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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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