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

1,714 papers, page 3 of 35

101. NeurIPS 2026Fast and Consistent Structure Learning in Graphical Models via Approximate Cross-Validation
102. NeurIPS 2026FAST-Brain: A Flow-Aligned Spatio-Temporal Surrogate Brain Model
103. NeurIPS 2026Ferrogen: Generative Pipeline for Guided Search of Novel Ferroelectric Material for Logic and Memory
104. NeurIPS 2026Few-Step Cofolding with All-Atom Flow Maps
105. NeurIPS 2026Fine-Tuning Improves Information Conveyance in Language Models
106. NeurIPS 2026Fix the Structural Bottleneck: Context Compression via Explicit Information Transmission
107. NeurIPS 2026Fixed-Point Masked Generative Modeling
108. NeurIPS 2026Flowette: Flow Matching with Graphette Priors for Graph Generation
109. NeurIPS 2026FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models
110. NeurIPS 2026FoMo: Forking Moment in Generative Trajectory as a Perceptual Distance
111. NeurIPS 2026Frank-LoRA: Federated Rank-Aware LoRA for Fine-Tuning Large Models
112. NeurIPS 2026From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows
113. NeurIPS 2026From Jumps to Signatures: a Generative Method for Temporal Point Processes
114. NeurIPS 2026From Static Geometry to Dynamical Singularity: Detecting Memorization in Diffusion Models via Score Evolution
115. NeurIPS 2026Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering with Generative Optimization
116. NeurIPS 2026Full-Sequence Masked Diffusion for Generative Recommendation
117. NeurIPS 2026G2Fusion: Geometric-to-Generative Image Fusion via Registration-Restoration Evolution
118. NeurIPS 2026GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction
119. NeurIPS 2026GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators
120. NeurIPS 2026Generalization Dynamics of Linear Diffusion Models
121. NeurIPS 2026Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
122. NeurIPS 2026Generating Financial Time Series by Matching Random Convolutional Features
123. NeurIPS 2026Generative Active Learning via Bayesian Acquisition for Improving the Efficiency of Synthetic Data
124. NeurIPS 2026Generative Actor-Critic with Soft Bridge Policies
125. NeurIPS 2026Generative Conformal Prediction with Optimized Coverage Allocation
126. NeurIPS 2026Generative Cross-Entropy: A Strictly Proper Loss for Data-Efficient Classification
127. NeurIPS 2026Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
128. NeurIPS 2026Generative Modeling via Drifting
129. NeurIPS 2026Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport
130. NeurIPS 2026GenRec: Knowing Where to Reconstruct and Where to Generate
131. NeurIPS 2026GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction
132. NeurIPS 2026Geometric Velocity Regularity for Flow Matching on Manifold-Concentrated Data
133. NeurIPS 2026gfnx: Fast and Scalable Library for Generative Flow Networks in JAX
134. NeurIPS 2026GPA: Generative Population Annealing for Test-Time Sequence Design with Pretrained Generative Models
135. NeurIPS 2026Gradient Routing Localizes and Removes Unintended Behaviors in RL
136. NeurIPS 2026Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing
137. NeurIPS 2026HaNDF: Object-Conditioned Geometric Neural Hand Distance Fields
138. NeurIPS 2026HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data
139. NeurIPS 2026Hit Expansion via Localized Exploration of Synthesizable Chemical Space
140. NeurIPS 2026Horizontal Diffusion Models: Score-based Generative Modeling on Frame-Connection Geometry
141. NeurIPS 2026Hypergraph Generation via Structured Stochastic Diffusion
142. NeurIPS 2026HyperTransport: Amortized Conditioning of T2I Generative Models
143. NeurIPS 2026HyperVQ: Enabling Hyperprior Entropy Modeling for VQ-Based Generative Image Compression
144. NeurIPS 2026Idempotency Exposes Consistency Problems in Sparse Autoencoders
145. NeurIPS 2026Imagine, Don't Narrate: The Generative Bottleneck in World Models of Interaction Dynamics
146. NeurIPS 2026Improving Function Space Flow Matching with Kernel Optimal Transport
147. NeurIPS 2026Incorporating Hierarchical Semantics in Sparse Autoencoder Architectures
148. NeurIPS 2026Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
149. NeurIPS 2026Inference-Time Self-Aligned Drifting for Few-Step Flow Matching
150. NeurIPS 2026Inferotemporal Cortex Collaborates Before It Codes: Non-Serial Inter-Area Synergy in the Macaque Ventral Stream

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.