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

1,430 papers, page 24 of 29

1151. NeurIPS 2026Discrete Diffusion Models Exploit Asymmetry to Solve Lookahead Planning Tasks
1152. NeurIPS 2026Discrete Diffusion Playground: A 2D Benchmark for Discrete Generative Models
1153. NeurIPS 2026Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
1154. NeurIPS 2026Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors
1155. NeurIPS 2026Disentanglement as Identifiable Pushforward Factorisation
1156. NeurIPS 2026Displacement Geometry Captures Platonic Shared Reality Across Models and Modalities
1157. NeurIPS 2026Do Sparse Autoencoders Learn Meaningful Concept Hierarchies?
1158. NeurIPS 2026dRAE: Representation Autoencoder with Hyper-Spherical Codes
1159. NeurIPS 2026DrPO: Drifting Preference Optimization for One-Step Generative Models
1160. NeurIPS 2026Dual-Contrastive Sparse Autoencoders Reveal Features of Musical Interpretation
1161. NeurIPS 2026DualDrift: Combining Forward and Reverse Drifts for One-Step Generative Modeling
1162. NeurIPS 2026DySurface: Consistent 4D Surface Reconstruction via Bridging Explicit Gaussians and Implicit Functions
1163. NeurIPS 2026E-GEO: A Testbed for Generative Engine Optimization in E-Commerce
1164. NeurIPS 2026Efficient Off-Policy RL for Video Generation via Forward-Consistent Reward Matching
1165. NeurIPS 2026Efficient Reasoning via Constrained Optimization in Latent Space
1166. NeurIPS 2026Emergence of Distortions in High-Dimensional Guided Diffusion Models
1167. NeurIPS 2026Emergence, Retention and Mitigation of Ill-conditioning due to Basis Lifting in KANs
1168. NeurIPS 2026Empowering Masked Diffusion Models to Self-Correct with Leave-One-Out Transformers
1169. NeurIPS 2026Enabling Denoising Score Matching Type Training for Manifold Diffusion Model via Momentum and Splitting
1170. NeurIPS 2026End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
1171. NeurIPS 2026Energy-Tweedie: Score meets Score, Energy meets Energy
1172. NeurIPS 2026Enhanced convergence guarantees of score-based generative models in $\mathcal{W}_2$-distance beyond log-concavity
1173. NeurIPS 2026Entropy Distribution as a Fingerprint for Hallucinations in Generative Models
1174. NeurIPS 2026Escaping the Curse of Dimensionality in One‑Step Flow-Based Generative Models
1175. NeurIPS 2026Every Measurement, Every Direction, All at Once: Multimodal Flow Matching for Molecules and Spectra
1176. NeurIPS 2026Exact Channel Decoupling via Joint Diagonalization and Uniform Splicing for Diffusion Transformer Quantization
1177. NeurIPS 2026Expanding Flow Maps
1178. NeurIPS 2026Expected Batch Optimal Transport Plans and Consequences for Flow Matching
1179. NeurIPS 2026FaceProbe: Recovering HDR Environment Map via Masked Diffusion and Physical Preference
1180. NeurIPS 2026Factored Generative Models through Mechanism Diversity
1181. NeurIPS 2026Fast and Accurate Probing of In-Training LLMs' Downstream Performances
1182. NeurIPS 2026Fast and Consistent Structure Learning in Graphical Models via Approximate Cross-Validation
1183. NeurIPS 2026FAST-Brain: A Flow-Aligned Spatio-Temporal Surrogate Brain Model
1184. NeurIPS 2026Ferrogen: Generative Pipeline for Guided Search of Novel Ferroelectric Material for Logic and Memory
1185. NeurIPS 2026Few-Step Cofolding with All-Atom Flow Maps
1186. NeurIPS 2026Fine-Tuning Improves Information Conveyance in Language Models
1187. NeurIPS 2026Fix the Structural Bottleneck: Context Compression via Explicit Information Transmission
1188. NeurIPS 2026Fixed-Point Masked Generative Modeling
1189. NeurIPS 2026Flowette: Flow Matching with Graphette Priors for Graph Generation
1190. NeurIPS 2026FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models
1191. NeurIPS 2026FoMo: Forking Moment in Generative Trajectory as a Perceptual Distance
1192. NeurIPS 2026Frank-LoRA: Federated Rank-Aware LoRA for Fine-Tuning Large Models
1193. NeurIPS 2026From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows
1194. NeurIPS 2026From Jumps to Signatures: a Generative Method for Temporal Point Processes
1195. NeurIPS 2026From Static Geometry to Dynamical Singularity: Detecting Memorization in Diffusion Models via Score Evolution
1196. NeurIPS 2026Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering with Generative Optimization
1197. NeurIPS 2026Full-Sequence Masked Diffusion for Generative Recommendation
1198. NeurIPS 2026G2Fusion: Geometric-to-Generative Image Fusion via Registration-Restoration Evolution
1199. NeurIPS 2026GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction
1200. NeurIPS 2026GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators

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.