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

604 papers, page 5 of 13

201. ICML 2026Generative Augmented Inference
202. ICML 2026Generative Inverse Design with Abstention via Diagonal Flow Matching
203. ICML 2026Generative Large Neighborhood Search: Scalable Set Cover Optimization via Discrete Diffusion
204. ICML 2026Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients
205. ICML 2026Generative Modeling of Irregular Time Series via SDE-Induced Continuous-Discrete Variational Inference
206. ICML 2026Generative Online Reinforcement Learning
207. ICML 2026Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
208. ICML 2026Geodesic Flow Matching for Denoising High-Dimensional Structured Representations
209. ICML 2026GeoFlow: Geo-Aware Modeling of Inter-Area Relationships in Origin-Destination Flow Prediction and Generation
210. ICML 2026Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts
211. ICML 2026Geometric Decoupling: Diagnosing the Structural Instability of Latent
212. ICML 2026Geometry-Aware Probabilistic Circuits via Voronoi Tessellations
213. ICML 2026Geometry-Aware Tabular Diffusion
214. ICML 2026Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping
215. ICML 2026GEPC: Group-Equivariant Posterior Consistency for Out-of-Distribution Detection in Diffusion Models
216. ICML 2026GFedCL: Graph-Based Federated Continual Learning with Spatial and Temporal Awareness
217. ICML 2026GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation
218. ICML 2026GLAD: Bidirectional Structure-Attribute Alignment via Latent Graph Diffusion Models
219. ICML 2026Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
220. ICML 2026Gradient Preconditioning for Efficient and Reliable Reward-Guided Generation
221. ICML 2026Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Model
222. ICML 2026GraphP-FL: Personalized Federated Graph Learning via Dynamic Structure Awareness and Fisher Information Elastic Alignment
223. ICML 2026GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
224. ICML 2026GuidedBridge: Training-freely Improving Bridge Models with Prior Guidance
225. ICML 2026Hard-Constrained Graph Generation with Discrete-Projection Diffusion
226. ICML 2026HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference
227. ICML 2026Heterogeneous Customizable Personalized Federated Fine-Tuning Approach for Large Language Models
228. ICML 2026High-accuracy and dimension-free sampling with diffusions
229. ICML 2026High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent Confounders
230. ICML 2026HilbertA: Hilbert-Curve–Aligned Sparse Attention for 2D Structured Data
231. ICML 2026HIVE-3D: Hierarchical Voxel Enhancement for High-Quality 3D Scene Generation
232. ICML 2026Hölder++: Improving Quality-Coherence Trade-off in Multimodal VAEs
233. ICML 2026How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance
234. ICML 2026Hyperbolic RQ-VAE enhanced Generative Recommendation with Differential-Length Codebook Strategy
235. ICML 2026Identifiable Equivariant Networks are Layerwise Equivariant
236. ICML 2026IDLM: Inverse-distilled Diffusion Language Models
237. ICML 2026Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness
238. ICML 2026Improving Classifier-Free Guidance of Flow Matching via Manifold Projection
239. ICML 2026Improving LLM-Based Recommenders with Conservative Generative Flow Networks
240. ICML 2026Improving Sampling for Masked Diffusion Models via Information Gain
241. ICML 2026In-Context Generation with Regional Constraints for Instructional Video Editing
242. ICML 2026In-Context Universal Approximation, Compositional Generalization, and Algorithm Emulation
243. ICML 2026Independent Component Discovery in Temporal Count Data
244. ICML 2026InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames
245. ICML 2026Inference-time optimization for experiment-grounded protein ensemble generation
246. ICML 2026Infinite Mask Diffusion for Few-Step Distillation
247. ICML 2026Infinite-Dimensional Generative Diffusions via Doob’s h-Transform
248. ICML 2026Infinite-Precision Autoregressive Modeling for Vector Graphics and Layouts
249. ICML 2026Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory
250. ICML 2026Information-Geometric Adaptive Sampling for Graph Diffusion

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.