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

303 papers, page 4 of 7

151. ICLR 2026Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing
152. ICLR 2026Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
153. ICLR 2026LABEL-FREE MITIGATION OF SPURIOUS CORRELATIONS IN VLMS USING SPARSE AUTOENCODERS
154. ICLR 2026Latent Denoising Makes Good Tokenizers
155. ICLR 2026Latent Diffusion Model without Variational Autoencoder
156. ICLR 2026Latent Stochastic Interpolants
157. ICLR 2026Latent Wavelet Diffusion For Ultra High-Resolution Image Synthesis
158. ICLR 2026LayerSync: Self-aligning Intermediate Layers
159. ICLR 2026Learn to Guide Your Diffusion Model
160. ICLR 2026Learnable Sparsity for Vision Generative Models
161. ICLR 2026Learning a distance measure from the information-estimation geometry of data
162. ICLR 2026Learning Correlated Reward Models: Statistical Barriers and Opportunities
163. ICLR 2026Learning multimodal dictionary decompositions with group-sparse autoencoders
164. ICLR 2026Learning on a Razor’s Edge: Identifiability and Singularity of Polynomial Neural Networks
165. ICLR 2026Learning Retrieval Models with Sparse Autoencoders
166. ICLR 2026Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement
167. ICLR 2026Light-X: Generative 4D Video Rendering with Camera and Illumination Control
168. ICLR 2026LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
169. ICLR 2026LLM Pretraining with Continuous Concepts
170. ICLR 2026LLMs Can Hide Text in Other Text of the Same Length
171. ICLR 2026Loopholing Discrete Diffusion: Deterministic Bypass of the Sampling Wall
172. ICLR 2026LS-Merge: Merging Language Models in Latent Space
173. ICLR 2026Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry
174. ICLR 2026MAS$^2$: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems
175. ICLR 2026Masked Generative Policy for Robotic Control
176. ICLR 2026Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders
177. ICLR 2026Matched Data, Better Models: Target Aligned Data Filtering with Sparse Features
178. ICLR 2026Measurement Score-Based Diffusion Model
179. ICLR 2026MemGen: Weaving Generative Latent Memory for Self-Evolving Agents
180. ICLR 2026Mirror Flow Matching with Heavy-Tailed Priors for Generative Modeling on Convex Domains
181. ICLR 2026Mitigating Noise Shift in Denoising Generative Models with Noise Awareness Guidance
182. ICLR 2026Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment
183. ICLR 2026Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter
184. ICLR 2026MOSS: Efficient and Accurate FP8 LLM Training with Microscaling and Automatic Scaling
185. ICLR 2026Motion Prior Distillation in Time Reversal Sampling for Generative Inbetweening
186. ICLR 2026Much Ado About Noising: Dispelling the Myths of Generative Robotic Control
187. ICLR 2026Multiverse Mechanica: A Testbed for Learning Game Mechanics via Counterfactual Worlds
188. ICLR 2026MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection
189. ICLR 2026MVCustom: Multi-View Customized Diffusion via Geometric Latent Rendering and Completion
190. ICLR 2026Navigating the Latent Space Dynamics of Neural Models
191. ICLR 2026NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
192. ICLR 2026Neyman-Pearson Classification under Both Null and Alternative Distributions Shift
193. ICLR 2026NFT: Bridging Supervised Learning and Reinforcement Learning in Math Reasoning
194. ICLR 2026NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models
195. ICLR 2026On Discriminative vs. Generative classifiers: Rethinking MLLMs for Action Understanding
196. ICLR 2026On the Limits of Sparse Autoencoders: A Theoretical Framework and Reweighted Remedy
197. ICLR 2026Online Black-Box Prompt Optimization with Regret Guarantees under Noisy Feedback
198. ICLR 2026Online Decision Making with Generative Action Sets
199. ICLR 2026Overshoot and Shrinkage in Classifier-Free Guidance: From Theory to Practice
200. ICLR 2026P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling

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.