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 24 of 27

1151. ICML 2023Hindsight Learning for MDPs with Exogenous Inputs
1152. ICML 2023How much does Initialization Affect Generalization?
1153. ICML 2023Hyperbolic Representation Learning: Revisiting and Advancing
1154. ICML 2023Hyperparameters in Reinforcement Learning and How To Tune Them
1155. ICML 2023Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
1156. ICML 2023Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process
1157. ICML 2023Improving Graph Generation by Restricting Graph Bandwidth
1158. ICML 2023Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
1159. ICML 2023Individually Fair Learning with One-Sided Feedback
1160. ICML 2023Inflow, Outflow, and Reciprocity in Machine Learning
1161. ICML 2023Input Perturbation Reduces Exposure Bias in Diffusion Models
1162. ICML 2023Jump-Start Reinforcement Learning
1163. ICML 2023KDEformer: Accelerating Transformers via Kernel Density Estimation
1164. ICML 2023Latent Traversals in Generative Models as Potential Flows
1165. ICML 2023LeadFL: Client Self-Defense against Model Poisoning in Federated Learning
1166. ICML 2023Learn to Accumulate Evidence from All Training Samples: Theory and Practice
1167. ICML 2023Learning Belief Representations for Partially Observable Deep RL
1168. ICML 2023Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
1169. ICML 2023Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
1170. ICML 2023Learning Globally Smooth Functions on Manifolds
1171. ICML 2023Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
1172. ICML 2023Learning Instance-Specific Augmentations by Capturing Local Invariances
1173. ICML 2023Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
1174. ICML 2023Learning Preconditioners for Conjugate Gradient PDE Solvers
1175. ICML 2023Learning the Dynamics of Sparsely Observed Interacting Systems
1176. ICML 2023Learning to Design Analog Circuits to Meet Threshold Specifications
1177. ICML 2023Learning to Initiate and Reason in Event-Driven Cascading Processes
1178. ICML 2023Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
1179. ICML 2023Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
1180. ICML 2023Learning Unnormalized Statistical Models via Compositional Optimization
1181. ICML 2023Learning useful representations for shifting tasks and distributions
1182. ICML 2023Learning-augmented private algorithms for multiple quantile release
1183. ICML 2023Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
1184. ICML 2023LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
1185. ICML 2023Lower Bounds for Learning in Revealing POMDPs
1186. ICML 2023MALTS: Matching After Learning to Stretch
1187. ICML 2023Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations
1188. ICML 2023Markovian Gaussian Process Variational Autoencoders
1189. ICML 2023Maximal Initial Learning Rates in Deep ReLU Networks
1190. ICML 2023Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
1191. ICML 2023Meta-Learning the Inductive Bias of Simple Neural Circuits
1192. ICML 2023Metagenomic Binning using Connectivity-constrained Variational Autoencoders
1193. ICML 2023Minimal Width for Universal Property of Deep RNN
1194. ICML 2023Minimizing Trajectory Curvature of ODE-based Generative Models
1195. ICML 2023Model Transferability with Responsive Decision Subjects
1196. ICML 2023Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
1197. ICML 2023MODeL: Memory Optimizations for Deep Learning
1198. ICML 2023MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
1199. ICML 2023Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
1200. ICML 2023Multi-View Masked World Models for Visual Robotic Manipulation

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.