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

1,304 papers, page 3 of 27

101. ICML 2026CRAG: Can 3D Generative Models Help 3D Assembly?
102. ICML 2026CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM
103. ICML 2026Curated Synthetic Data Doesn’t Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences
104. ICML 2026d2: Improving Reasoning in Diffusion Language Models via Trajectory Likelihood Estimation
105. ICML 2026DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants
106. ICML 2026DAPD: Dependency-Aware Parallel Decoding via Attention for Diffusion LLMs
107. ICML 2026Decomposing Out-of-Distribution Error in Conditional Flow Matching via Wasserstein Geometry
108. ICML 2026Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
109. ICML 2026Deep Forcing: Training-Free Long Video Generation with Deep Sink and Participative Compression
110. ICML 2026Deformba: Vision State Space Model with Adaptive State Fusion
111. ICML 2026DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?
112. ICML 2026Detecting Perspective Shifts in Multi-Agent Systems
113. ICML 2026Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps
114. ICML 2026DiasR: Dual-Modal Identity-Anchored Sparse Routing for Efficient Multi-Subject Video Generation
115. ICML 2026DiFA: Inference-Time Forward-Process Alignment for Diffusion Models
116. ICML 2026Differential syntactic and semantic encoding in LLMs
117. ICML 2026DiffStyle3D: Consistent 3D Gaussian Stylization via Attention Optimization
118. ICML 2026Diffusion Bridge or Flow Matching? A Unifying Framework and Comparative Analysis
119. ICML 2026Diffusion Controller: Framework, Algorithms and Parameterization
120. ICML 2026Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees
121. ICML 2026Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
122. ICML 2026Diffusion Models Preferentially Memorize Prototypical Examples or: Why Does My Diffusion Model Love Slop?
123. ICML 2026Dimension-free convergence of diffusion models for approximate Gaussian mixtures
124. ICML 2026Dimensional Collapse in Transformer Attention Outputs: A Challenge for Sparse Dictionary Learning
125. ICML 2026DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing
126. ICML 2026DiScoFormer: Plug-In Density and Score Estimation with Transformers
127. ICML 2026Distribution Matching Variational AutoEncoder
128. ICML 2026Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models
129. ICML 2026DNACHUNKER: Learnable Tokenization for DNA Language Models
130. ICML 2026Domain Restriction via SAE Multi-Layer Transitions
131. ICML 2026Draft-and-Audit Reinforcement Learning for Optimization Modeling
132. ICML 2026Dual Optimal Transport for Multi-Concept Composition: Structural Alignment and Texture Injection in Diffusion Models
133. ICML 2026DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention
134. ICML 2026DynaMem: Consistent Long Video Generation via Hierarchical Memory and Motion Priors
135. ICML 2026ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios
136. ICML 2026Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers
137. ICML 2026Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation
138. ICML 2026Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing
139. ICML 2026Efficient Diffusion Models via Time Step Optimization with Consistent Training and Inference Constraints
140. ICML 2026Efficient Reasoning with Hidden Thinking
141. ICML 2026Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
142. ICML 2026Efficient Test-time Inference for Generative Planning Models with OCL Search
143. ICML 2026Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization
144. ICML 2026Efficiently Learning Drifting Halfspaces with Massart Noise
145. ICML 2026EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video
146. ICML 2026EigenCache: Rethinking Diffusion Acceleration as Covariance-Optimal Forecasting and Submodular Information Allocation
147. ICML 2026ElicitR: Unlocking Latent Reasoning in Dense Retrievers via Generative Regularization
148. ICML 2026Embedding-perturbed Exploration Preference Optimization for Flow Models
149. ICML 2026End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer
150. ICML 2026EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions

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.