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

1,216 papers, page 18 of 25

  1. NeurIPS 2023Lexinvariant Language Models
  2. NeurIPS 2023LICO: Explainable Models with Language-Image COnsistency
  3. NeurIPS 2023LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion
  4. NeurIPS 2023List and Certificate Complexities in Replicable Learning
  5. NeurIPS 2023LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing
  6. NeurIPS 2023LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition
  7. NeurIPS 2023Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning
  8. NeurIPS 2023Loss Decoupling for Task-Agnostic Continual Learning
  9. NeurIPS 2023Loss Dynamics of Temporal Difference Reinforcement Learning
  10. NeurIPS 2023Lossy Image Compression with Conditional Diffusion Models
  11. NeurIPS 2023Lung250M-4B: A Combined 3D Dataset for CT- and Point Cloud-Based Intra-Patient Lung Registration
  12. NeurIPS 2023Markovian Sliced Wasserstein Distances: Beyond Independent Projections
  13. NeurIPS 2023Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
  14. NeurIPS 2023Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
  15. NeurIPS 2023Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits
  16. NeurIPS 2023Maximum State Entropy Exploration using Predecessor and Successor Representations
  17. NeurIPS 2023May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
  18. NeurIPS 2023MedSat: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery
  19. NeurIPS 2023Meek Separators and Their Applications in Targeted Causal Discovery
  20. NeurIPS 2023Meet in the Middle: A New Pre-training Paradigm
  21. NeurIPS 2023Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning
  22. NeurIPS 2023Meta-Learning with Neural Bandit Scheduler
  23. NeurIPS 2023Metis: Understanding and Enhancing In-Network Regular Expressions
  24. NeurIPS 2023Metropolis Sampling for Constrained Diffusion Models
  25. NeurIPS 2023MGDD: A Meta Generator for Fast Dataset Distillation
  26. NeurIPS 2023Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
  27. NeurIPS 2023Minimum Description Length and Generalization Guarantees for Representation Learning
  28. NeurIPS 2023Mirror Diffusion Models for Constrained and Watermarked Generation
  29. NeurIPS 2023Mitigating Test-Time Bias for Fair Image Retrieval
  30. NeurIPS 2023Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
  31. NeurIPS 2023MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
  32. NeurIPS 2023Mnemosyne: Learning to Train Transformers with Transformers
  33. NeurIPS 2023Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
  34. NeurIPS 2023Model Spider: Learning to Rank Pre-Trained Models Efficiently
  35. NeurIPS 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
  36. NeurIPS 2023Model-enhanced Vector Index
  37. NeurIPS 2023Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
  38. NeurIPS 2023Module-wise Training of Neural Networks via the Minimizing Movement Scheme
  39. NeurIPS 2023Multi Time Scale World Models
  40. NeurIPS 2023Multi-Object Representation Learning via Feature Connectivity and Object-Centric Regularization
  41. NeurIPS 2023Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
  42. NeurIPS 2023Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
  43. NeurIPS 2023MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks
  44. NeurIPS 2023NAP: Neural 3D Articulated Object Prior
  45. NeurIPS 2023NAS-X: Neural Adaptive Smoothing via Twisting
  46. NeurIPS 2023NCDL: A Framework for Deep Learning on non-Cartesian Lattices
  47. NeurIPS 2023Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
  48. NeurIPS 2023Near-optimal learning with average Hölder smoothness
  49. NeurIPS 2023Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
  50. NeurIPS 2023Neural Image Compression: Generalization, Robustness, and Spectral Biases

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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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