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

1,714 papers, page 31 of 35

1501. NeurIPS 2023RanPAC: Random Projections and Pre-trained Models for Continual Learning
1502. NeurIPS 2023Recommender Systems with Generative Retrieval
1503. NeurIPS 2023REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling
1504. NeurIPS 2023Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
1505. NeurIPS 2023Regularization properties of adversarially-trained linear regression
1506. NeurIPS 2023Regularizing Neural Networks with Meta-Learning Generative Models
1507. NeurIPS 2023Rehearsal Learning for Avoiding Undesired Future
1508. NeurIPS 2023ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence
1509. NeurIPS 2023Reinforcement Learning with Fast and Forgetful Memory
1510. NeurIPS 2023RELIC: Reproducibility and Extension on LIC metric in quantifying bias in captioning models
1511. NeurIPS 2023Replicable Clustering
1512. NeurIPS 2023RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
1513. NeurIPS 2023Representational Strengths and Limitations of Transformers
1514. NeurIPS 2023Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests
1515. NeurIPS 2023Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone
1516. NeurIPS 2023Resetting the Optimizer in Deep RL: An Empirical Study
1517. NeurIPS 2023Residual Q-Learning: Offline and Online Policy Customization without Value
1518. NeurIPS 2023ResMem: Learn what you can and memorize the rest
1519. NeurIPS 2023Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning
1520. NeurIPS 2023ResoNet: Noise-Trained Physics-Informed MRI Off-Resonance Correction
1521. NeurIPS 2023Restart Sampling for Improving Generative Processes
1522. NeurIPS 2023Rethinking Conditional Diffusion Sampling with Progressive Guidance
1523. NeurIPS 2023Rethinking Gauss-Newton for learning over-parameterized models
1524. NeurIPS 2023Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
1525. NeurIPS 2023Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules
1526. NeurIPS 2023Reverse Engineering Self-Supervised Learning
1527. NeurIPS 2023Reversible and irreversible bracket-based dynamics for deep graph neural networks
1528. NeurIPS 2023Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
1529. NeurIPS 2023Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations
1530. NeurIPS 2023Revisiting Implicit Differentiation for Learning Problems in Optimal Control
1531. NeurIPS 2023Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective
1532. NeurIPS 2023Revisiting the Evaluation of Image Synthesis with GANs
1533. NeurIPS 2023Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View
1534. NeurIPS 2023Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
1535. NeurIPS 2023REx: Data-Free Residual Quantization Error Expansion
1536. NeurIPS 2023RGMIL: Guide Your Multiple-Instance Learning Model with Regressor
1537. NeurIPS 2023Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
1538. NeurIPS 2023Robust and Actively Secure Serverless Collaborative Learning
1539. NeurIPS 2023Robust Learning with Progressive Data Expansion Against Spurious Correlation
1540. NeurIPS 2023Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
1541. NeurIPS 2023SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise
1542. NeurIPS 2023Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling
1543. NeurIPS 2023Sample Complexity of Forecast Aggregation
1544. NeurIPS 2023Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
1545. NeurIPS 2023Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
1546. NeurIPS 2023Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
1547. NeurIPS 2023SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery
1548. NeurIPS 2023SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning
1549. NeurIPS 2023Scalarization for Multi-Task and Multi-Domain Learning at Scale
1550. NeurIPS 2023Scale Alone Does not Improve Mechanistic Interpretability in Vision Models

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.