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

349 papers, page 4 of 7

151. NeurIPS 2024How Diffusion Models Learn to Factorize and Compose
152. NeurIPS 2024Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
153. NeurIPS 2024HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning
154. NeurIPS 2024HyperLogic: Enhancing Diversity and Accuracy in Rule Learning with HyperNets
155. NeurIPS 2024Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention
156. NeurIPS 2024Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models
157. NeurIPS 2024Image Reconstruction Via Autoencoding Sequential Deep Image Prior
158. NeurIPS 2024Image Understanding Makes for A Good Tokenizer for Image Generation
159. NeurIPS 2024Improving Sparse Decomposition of Language Model Activations with Gated Sparse Autoencoders
160. NeurIPS 2024Improving the Training of Rectified Flows
161. NeurIPS 2024Improving Viewpoint-Independent Object-Centric Representations through Active Viewpoint Selection
162. NeurIPS 2024Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models
163. NeurIPS 2024Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space
164. NeurIPS 2024Instruction-Guided Visual Masking
165. NeurIPS 2024InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction
166. NeurIPS 2024Invisible Image Watermarks Are Provably Removable Using Generative AI
167. NeurIPS 2024Is Programming by Example Solved by LLMs?
168. NeurIPS 2024Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling
169. NeurIPS 2024L4GM: Large 4D Gaussian Reconstruction Model
170. NeurIPS 2024LAM3D: Large Image-Point Clouds Alignment Model for 3D Reconstruction from Single Image
171. NeurIPS 2024Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models
172. NeurIPS 2024Language-Driven Interactive Traffic Trajectory Generation
173. NeurIPS 2024LCGen: Mining in Low-Certainty Generation for View-consistent Text-to-3D
174. NeurIPS 2024Learning Diffusion Priors from Observations by Expectation Maximization
175. NeurIPS 2024Learning Distributions on Manifolds with Free-Form Flows
176. NeurIPS 2024Learning from Pattern Completion: Self-supervised Controllable Generation
177. NeurIPS 2024Learning Superconductivity from Ordered and Disordered Material Structures
178. NeurIPS 2024Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks
179. NeurIPS 2024Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems
180. NeurIPS 2024Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching
181. NeurIPS 2024Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals
182. NeurIPS 2024Leveraging Drift to Improve Sample Complexity of Variance Exploding Diffusion Models
183. NeurIPS 2024LG-VQ: Language-Guided Codebook Learning
184. NeurIPS 2024Light Unbalanced Optimal Transport
185. NeurIPS 2024Linguistic Collapse: Neural Collapse in (Large) Language Models
186. NeurIPS 2024LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models
187. NeurIPS 2024LLM Circuit Analyses Are Consistent Across Training and Scale
188. NeurIPS 2024Local Curvature Smoothing with Stein's Identity for Efficient Score Matching
189. NeurIPS 2024Local to Global: Learning Dynamics and Effect of Initialization for Transformers
190. NeurIPS 2024Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild
191. NeurIPS 2024Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models
192. NeurIPS 2024Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT
193. NeurIPS 2024MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models
194. NeurIPS 2024MassSpecGym: A benchmark for the discovery and identification of molecules
195. NeurIPS 2024Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models
196. NeurIPS 2024Mesa-Extrapolation: A Weave Position Encoding Method for Enhanced Extrapolation in LLMs
197. NeurIPS 2024MeshXL: Neural Coordinate Field for Generative 3D Foundation Models
198. NeurIPS 2024Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials
199. NeurIPS 2024Metric Flow Matching for Smooth Interpolations on the Data Manifold
200. NeurIPS 2024Metric Space Magnitude for Evaluating the Diversity of Latent Representations

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.