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 25 of 27

1201. ICML 2023Multiply Robust Off-policy Evaluation and Learning under Truncation by Death
1202. ICML 2023Multisample Flow Matching: Straightening Flows with Minibatch Couplings
1203. ICML 2023Muse: Text-To-Image Generation via Masked Generative Transformers
1204. ICML 2023Near-Optimal $\Phi$-Regret Learning in Extensive-Form Games
1205. ICML 2023Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
1206. ICML 2023Nearly-tight Bounds for Deep Kernel Learning
1207. ICML 2023Neural Diffusion Processes
1208. ICML 2023No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation
1209. ICML 2023Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
1210. ICML 2023On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization
1211. ICML 2023On Investigating the Conservative Property of Score-Based Generative Models
1212. ICML 2023On Kinetic Optimal Probability Paths for Generative Models
1213. ICML 2023On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
1214. ICML 2023On Preemption and Learning in Stochastic Scheduling
1215. ICML 2023On Provable Copyright Protection for Generative Models
1216. ICML 2023On the Connection Between MPNN and Graph Transformer
1217. ICML 2023On the Convergence Rate of Gaussianization with Random Rotations
1218. ICML 2023On the Functional Similarity of Robust and Non-Robust Neural Representations
1219. ICML 2023On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
1220. ICML 2023On the Relationship Between Explanation and Prediction: A Causal View
1221. ICML 2023Optimizing NOTEARS Objectives via Topological Swaps
1222. ICML 2023Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
1223. ICML 2023Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations
1224. ICML 2023Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
1225. ICML 2023Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
1226. ICML 2023PAC-Bayesian Generalization Bounds for Adversarial Generative Models
1227. ICML 2023Paging with Succinct Predictions
1228. ICML 2023Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
1229. ICML 2023Performative Reinforcement Learning
1230. ICML 2023Personalized Federated Learning with Inferred Collaboration Graphs
1231. ICML 2023Personalized Subgraph Federated Learning
1232. ICML 2023PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
1233. ICML 2023Poisoning Generative Replay in Continual Learning to Promote Forgetting
1234. ICML 2023Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach
1235. ICML 2023Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
1236. ICML 2023Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
1237. ICML 2023Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
1238. ICML 2023Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning
1239. ICML 2023Proper Losses for Discrete Generative Models
1240. ICML 2023Provably Learning Object-Centric Representations
1241. ICML 2023Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
1242. ICML 2023Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
1243. ICML 2023Quantile Credit Assignment
1244. ICML 2023Quantitative Universal Approximation Bounds for Deep Belief Networks
1245. ICML 2023Recasting Self-Attention with Holographic Reduced Representations
1246. ICML 2023Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
1247. ICML 2023Reflected Diffusion Models
1248. ICML 2023Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
1249. ICML 2023Regularizing Towards Soft Equivariance Under Mixed Symmetries
1250. ICML 2023Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition

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.