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

286 papers, page 4 of 6

151. ICML 2023Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
152. ICML 2023Learning Globally Smooth Functions on Manifolds
153. ICML 2023Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
154. ICML 2023Learning Instance-Specific Augmentations by Capturing Local Invariances
155. ICML 2023Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
156. ICML 2023Learning Preconditioners for Conjugate Gradient PDE Solvers
157. ICML 2023Learning the Dynamics of Sparsely Observed Interacting Systems
158. ICML 2023Learning to Design Analog Circuits to Meet Threshold Specifications
159. ICML 2023Learning to Initiate and Reason in Event-Driven Cascading Processes
160. ICML 2023Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
161. ICML 2023Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
162. ICML 2023Learning Unnormalized Statistical Models via Compositional Optimization
163. ICML 2023Learning useful representations for shifting tasks and distributions
164. ICML 2023Learning-augmented private algorithms for multiple quantile release
165. ICML 2023Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
166. ICML 2023LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
167. ICML 2023Lower Bounds for Learning in Revealing POMDPs
168. ICML 2023MALTS: Matching After Learning to Stretch
169. ICML 2023Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations
170. ICML 2023Markovian Gaussian Process Variational Autoencoders
171. ICML 2023Maximal Initial Learning Rates in Deep ReLU Networks
172. ICML 2023Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
173. ICML 2023Meta-Learning the Inductive Bias of Simple Neural Circuits
174. ICML 2023Metagenomic Binning using Connectivity-constrained Variational Autoencoders
175. ICML 2023Minimal Width for Universal Property of Deep RNN
176. ICML 2023Minimizing Trajectory Curvature of ODE-based Generative Models
177. ICML 2023Model Transferability with Responsive Decision Subjects
178. ICML 2023Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
179. ICML 2023MODeL: Memory Optimizations for Deep Learning
180. ICML 2023MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
181. ICML 2023Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
182. ICML 2023Multi-View Masked World Models for Visual Robotic Manipulation
183. ICML 2023Multiply Robust Off-policy Evaluation and Learning under Truncation by Death
184. ICML 2023Multisample Flow Matching: Straightening Flows with Minibatch Couplings
185. ICML 2023Muse: Text-To-Image Generation via Masked Generative Transformers
186. ICML 2023Near-Optimal $\Phi$-Regret Learning in Extensive-Form Games
187. ICML 2023Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
188. ICML 2023Nearly-tight Bounds for Deep Kernel Learning
189. ICML 2023Neural Diffusion Processes
190. ICML 2023No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation
191. ICML 2023Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
192. ICML 2023On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization
193. ICML 2023On Investigating the Conservative Property of Score-Based Generative Models
194. ICML 2023On Kinetic Optimal Probability Paths for Generative Models
195. ICML 2023On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
196. ICML 2023On Preemption and Learning in Stochastic Scheduling
197. ICML 2023On Provable Copyright Protection for Generative Models
198. ICML 2023On the Connection Between MPNN and Graph Transformer
199. ICML 2023On the Convergence Rate of Gaussianization with Random Rotations
200. ICML 2023On the Functional Similarity of Robust and Non-Robust Neural Representations

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.