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 4 of 13

151. ICML 2026Enhancing Cross-subject Emotion Recognition via Heterogeneous Distribution Augmentation and Collaborative Learning
152. ICML 2026Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos
153. ICML 2026Ensembling Sparse Autoencoders
154. ICML 2026Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation
155. ICML 2026Equalized Generative Treatment: Matching f-divergences for Fairness in Generative Models
156. ICML 2026Error Propagation Mechanisms and Compensation Strategies for Quantized Diffusion Models
157. ICML 2026Esoteric Language Models: A Family of Any-Order Diffusion LLMs
158. ICML 2026Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
159. ICML 2026EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization
160. ICML 2026Exploring and Exploiting Stability in Latent Flow Matching
161. ICML 2026Exploring Data-Free LoRA Transferability for Video Diffusion Models
162. ICML 2026Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance
163. ICML 2026Expo-GS: Exposure-Aware Signed Distance Function in Gaussian Splatting for High Dynamic Range
164. ICML 2026Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks
165. ICML 2026Factored Classifier-Free Guidance
166. ICML 2026FaPS: A General and Fast Training Method for Diffusion Models
167. ICML 2026Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
168. ICML 2026Fast and Scalable Analytical Diffusion
169. ICML 2026FAST-AR: Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention
170. ICML 2026FasterVAR: Plug-and-Play Acceleration for Visual Autoregressive Models
171. ICML 2026Feature-Aware (Hyper)graph Generation via Next-Scale Prediction
172. ICML 2026FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
173. ICML 2026FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
174. ICML 2026FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning
175. ICML 2026Fine-Tuning Masked Diffusion for Provable Self-Correction
176. ICML 2026Flash-VAED: Plug-and-Play VAE Decoders for Efficient Video Generation
177. ICML 2026Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model
178. ICML 2026Flexibility-Aware Geometric Latent Diffusion for Full-Atom Peptide Design
179. ICML 2026Float8@2bits: Entropy Coding Enables Data-Free Model Compression
180. ICML 2026Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients
181. ICML 2026Flow Sampling : Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
182. ICML 2026FluxNet: Learning Capacity-Constrained Local Transport Operators for Conservative and Bounded PDE Surrogates
183. ICML 2026FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance For Pruned Large Language Models
184. ICML 2026Focusing: View-Consistent Sparse Voxels for Efficient 3D VAE Training
185. ICML 2026Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking
186. ICML 2026Forward-Chaining Temporal Point Process
187. ICML 2026FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models
188. ICML 2026FPTQuant: Function-Preserving Transforms for LLM Quantization
189. ICML 2026From Extraction to Deduction: Resolving Functional Misalignment in RAG via a Collaborative Critic-Reasoner Framework
190. ICML 2026From Generative to Episodic: Sample-Efficient Replicable Reinforcement Learning
191. ICML 2026From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
192. ICML 2026From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors
193. ICML 2026FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds
194. ICML 2026GEM: Geometric Erasure by Contrastive Velocity Matching in Rectified Flows
195. ICML 2026GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
196. ICML 2026General and Efficient Steering of Unconditional Diffusion Models
197. ICML 2026Generalization Bounds for Discrete Diffusion: Statistical Advantage of Masking
198. ICML 2026Generalized Discrete Diffusion with Self-Correction
199. ICML 2026Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation
200. ICML 2026Generative Adaptation of Dynamics to Environmental Shifts via Weight-space 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.