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 5 of 6

201. ICML 2023On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
202. ICML 2023On the Relationship Between Explanation and Prediction: A Causal View
203. ICML 2023Optimizing NOTEARS Objectives via Topological Swaps
204. ICML 2023Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
205. ICML 2023Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations
206. ICML 2023Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
207. ICML 2023Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
208. ICML 2023PAC-Bayesian Generalization Bounds for Adversarial Generative Models
209. ICML 2023Paging with Succinct Predictions
210. ICML 2023Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
211. ICML 2023Performative Reinforcement Learning
212. ICML 2023Personalized Federated Learning with Inferred Collaboration Graphs
213. ICML 2023Personalized Subgraph Federated Learning
214. ICML 2023PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
215. ICML 2023Poisoning Generative Replay in Continual Learning to Promote Forgetting
216. ICML 2023Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach
217. ICML 2023Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
218. ICML 2023Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
219. ICML 2023Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
220. ICML 2023Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning
221. ICML 2023Proper Losses for Discrete Generative Models
222. ICML 2023Provably Learning Object-Centric Representations
223. ICML 2023Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
224. ICML 2023Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
225. ICML 2023Quantile Credit Assignment
226. ICML 2023Quantitative Universal Approximation Bounds for Deep Belief Networks
227. ICML 2023Recasting Self-Attention with Holographic Reduced Representations
228. ICML 2023Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
229. ICML 2023Reflected Diffusion Models
230. ICML 2023Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
231. ICML 2023Regularizing Towards Soft Equivariance Under Mixed Symmetries
232. ICML 2023Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
233. ICML 2023Restoration based Generative Models
234. ICML 2023Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
235. ICML 2023Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees
236. ICML 2023Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature
237. ICML 2023Revisiting Structured Variational Autoencoders
238. ICML 2023Robust and private stochastic linear bandits
239. ICML 2023Robust Collaborative Learning with Linear Gradient Overhead
240. ICML 2023Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
241. ICML 2023Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning
242. ICML 2023Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
243. ICML 2023Scaling Laws for Generative Mixed-Modal Language Models
244. ICML 2023SGD with Large Step Sizes Learns Sparse Features
245. ICML 2023Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
246. ICML 2023Solving High-Dimensional PDEs with Latent Spectral Models
247. ICML 2023SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot
248. ICML 2023Special Properties of Gradient Descent with Large Learning Rates
249. ICML 2023Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary
250. ICML 2023spred: Solving L1 Penalty with SGD

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.