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

1,216 papers, page 22 of 25

1051. NeurIPS 2023Scalarization for Multi-Task and Multi-Domain Learning at Scale
1052. NeurIPS 2023Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
1053. NeurIPS 2023Scaling Laws for Hyperparameter Optimization
1054. NeurIPS 2023Scaling MLPs: A Tale of Inductive Bias
1055. NeurIPS 2023Scaling Riemannian Diffusion Models
1056. NeurIPS 2023Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion
1057. NeurIPS 2023Schema-learning and rebinding as mechanisms of in-context learning and emergence
1058. NeurIPS 2023Score-based Generative Modeling through Stochastic Evolution Equations in Hilbert Spaces
1059. NeurIPS 2023Score-based Generative Models with Lévy Processes
1060. NeurIPS 2023Score-based Source Separation with Applications to Digital Communication Signals
1061. NeurIPS 2023SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models
1062. NeurIPS 2023Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
1063. NeurIPS 2023Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
1064. NeurIPS 2023Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
1065. NeurIPS 2023Semantic Image Synthesis with Unconditional Generator
1066. NeurIPS 2023Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
1067. NeurIPS 2023Semi-Implicit Denoising Diffusion Models (SIDDMs)
1068. NeurIPS 2023Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation
1069. NeurIPS 2023Semi-Supervised Domain Generalization with Known and Unknown Classes
1070. NeurIPS 2023Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots
1071. NeurIPS 2023Siamese Masked Autoencoders
1072. NeurIPS 2023SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
1073. NeurIPS 2023SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
1074. NeurIPS 2023Simple and Asymmetric Graph Contrastive Learning without Augmentations
1075. NeurIPS 2023Simple and Controllable Music Generation
1076. NeurIPS 2023Simple, Scalable and Effective Clustering via One-Dimensional Projections
1077. NeurIPS 2023SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
1078. NeurIPS 2023Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions
1079. NeurIPS 2023Skill-it! A data-driven skills framework for understanding and training language models
1080. NeurIPS 2023SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization
1081. NeurIPS 2023SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models
1082. NeurIPS 2023Slow and Weak Attractor Computation Embedded in Fast and Strong E-I Balanced Neural Dynamics
1083. NeurIPS 2023Smooth, exact rotational symmetrization for deep learning on point clouds
1084. NeurIPS 2023Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
1085. NeurIPS 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
1086. NeurIPS 2023Sorting with Predictions
1087. NeurIPS 2023Sparse Deep Learning for Time Series Data: Theory and Applications
1088. NeurIPS 2023Sparse Parameterization for Epitomic Dataset Distillation
1089. NeurIPS 2023Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning
1090. NeurIPS 2023Spectral Co-Distillation for Personalized Federated Learning
1091. NeurIPS 2023Spontaneous symmetry breaking in generative diffusion models
1092. NeurIPS 2023Squared Neural Families: A New Class of Tractable Density Models
1093. NeurIPS 2023SSL4EO-L: Datasets and Foundation Models for Landsat Imagery
1094. NeurIPS 2023Stability of Random Forests and Coverage of Random-Forest Prediction Intervals
1095. NeurIPS 2023Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
1096. NeurIPS 2023Stable Diffusion is Unstable
1097. NeurIPS 2023StableFDG: Style and Attention Based Learning for Federated Domain Generalization
1098. NeurIPS 2023Star-Shaped Denoising Diffusion Probabilistic Models
1099. NeurIPS 2023State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory
1100. NeurIPS 2023Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory

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.