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

139 papers, page 3 of 3

101. ICML 2024Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
102. ICML 2024Provably Robust DPO: Aligning Language Models with Noisy Feedback
103. ICML 2024Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
104. ICML 2024Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
105. ICML 2024Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
106. ICML 2024Recovering the Pre-Fine-Tuning Weights of Generative Models
107. ICML 2024Reflected Flow Matching
108. ICML 2024Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
109. ICML 2024Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
110. ICML 2024Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
111. ICML 2024RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
112. ICML 2024Robust and Conjugate Gaussian Process Regression
113. ICML 2024Sample as you Infer: Predictive Coding with Langevin Dynamics
114. ICML 2024Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
115. ICML 2024Self-Correcting Self-Consuming Loops for Generative Model Training
116. ICML 2024Single-Model Attribution of Generative Models Through Final-Layer Inversion
117. ICML 2024Sliced Wasserstein with Random-Path Projecting Directions
118. ICML 2024SMaRt: Improving GANs with Score Matching Regularity
119. ICML 2024Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models
120. ICML 2024SqueezeLLM: Dense-and-Sparse Quantization
121. ICML 2024Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
122. ICML 2024Stacking Deep Set Networks and Pooling by Quantiles
123. ICML 2024Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
124. ICML 2024Stochastic Interpolants with Data-Dependent Couplings
125. ICML 2024StrWAEs to Invariant Representations
126. ICML 2024Switched Flow Matching: Eliminating Singularities via Switching ODEs
127. ICML 2024The Emergence of Reproducibility and Consistency in Diffusion Models
128. ICML 2024The Good, The Bad, and Why: Unveiling Emotions in Generative AI
129. ICML 2024The Linear Representation Hypothesis and the Geometry of Large Language Models
130. ICML 2024The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright BreachesWithout Adjusting Finetuning Pipeline
131. ICML 2024Timer: Generative Pre-trained Transformers Are Large Time Series Models
132. ICML 2024Topological Neural Networks go Persistent, Equivariant, and Continuous
133. ICML 2024Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components
134. ICML 2024Towards Neural Architecture Search through Hierarchical Generative Modeling
135. ICML 2024Towards Scalable and Versatile Weight Space Learning
136. ICML 2024Towards Theoretical Understandings of Self-Consuming Generative Models
137. ICML 2024Transformers, parallel computation, and logarithmic depth
138. ICML 2024Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
139. ICML 2024Why do Variational Autoencoders Really Promote Disentanglement?

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.