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

1,304 papers, page 22 of 27

1051. ICML 2023Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
1052. ICML 2023Calibrating Multimodal Learning
1053. ICML 2023CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data
1054. ICML 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
1055. ICML 2023Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
1056. ICML 2023CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
1057. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
1058. ICML 2023Composer: Creative and Controllable Image Synthesis with Composable Conditions
1059. ICML 2023Compositional Exemplars for In-context Learning
1060. ICML 2023Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
1061. ICML 2023Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
1062. ICML 2023ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
1063. ICML 2023Conditionally Strongly Log-Concave Generative Models
1064. ICML 2023Conditions and Assumptions for Constraint-based Causal Structure Learning
1065. ICML 2023Consistency Models
1066. ICML 2023Continuation Path Learning for Homotopy Optimization
1067. ICML 2023ContraBAR: Contrastive Bayes-Adaptive Deep RL
1068. ICML 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
1069. ICML 2023Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
1070. ICML 2023Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders
1071. ICML 2023Counterfactual Identifiability of Bijective Causal Models
1072. ICML 2023Coupled Variational Autoencoder
1073. ICML 2023Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss
1074. ICML 2023Data-Copying in Generative Models: A Formal Framework
1075. ICML 2023DDGR: Continual Learning with Deep Diffusion-based Generative Replay
1076. ICML 2023Decentralized SGD and Average-direction SAM are Asymptotically Equivalent
1077. ICML 2023Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
1078. ICML 2023Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
1079. ICML 2023Deep Graph Representation Learning and Optimization for Influence Maximization
1080. ICML 2023Deep linear networks can benignly overfit when shallow ones do
1081. ICML 2023Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum
1082. ICML 2023Denoising MCMC for Accelerating Diffusion-Based Generative Models
1083. ICML 2023DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
1084. ICML 2023Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
1085. ICML 2023Deterministic equivalent and error universality of deep random features learning
1086. ICML 2023Differentiable and Transportable Structure Learning
1087. ICML 2023Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
1088. ICML 2023Diffusion Based Representation Learning
1089. ICML 2023Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
1090. ICML 2023Direct Parameterization of Lipschitz-Bounded Deep Networks
1091. ICML 2023Directed Chain Generative Adversarial Networks
1092. ICML 2023Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models
1093. ICML 2023Disentangled Generative Models for Robust Prediction of System Dynamics
1094. ICML 2023Distortion and Uncertainty Aware Loss for Panoramic Depth Completion
1095. ICML 2023Distributed Linear Bandits under Communication Constraints
1096. ICML 2023Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
1097. ICML 2023Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
1098. ICML 2023Do Machine Learning Models Learn Statistical Rules Inferred from Data?
1099. ICML 2023Doubly Optimal No-Regret Learning in Monotone Games
1100. ICML 2023Dropout Reduces Underfitting

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.