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

174 papers, page 4 of 4

151. ICLR 2024Single Motion Diffusion
152. ICLR 2024SliceGPT: Compress Large Language Models by Deleting Rows and Columns
153. ICLR 2024Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community
154. ICLR 2024Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models
155. ICLR 2024Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
156. ICLR 2024Sparse Autoencoders Find Highly Interpretable Features in Language Models
157. ICLR 2024STREAM: Spatio-TempoRal Evaluation and Analysis Metric for Video Generative Models
158. ICLR 2024Talk like a Graph: Encoding Graphs for Large Language Models
159. ICLR 2024TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
160. ICLR 2024The Generative AI Paradox: “What It Can Create, It May Not Understand”
161. ICLR 2024Towards Generative Abstract Reasoning: Completing Raven’s Progressive Matrix via Rule Abstraction and Selection
162. ICLR 2024Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
163. ICLR 2024Towards Non-Asymptotic Convergence for Diffusion-Based Generative Models
164. ICLR 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
165. ICLR 2024Transformer-VQ: Linear-Time Transformers via Vector Quantization
166. ICLR 2024TUVF: Learning Generalizable Texture UV Radiance Fields
167. ICLR 2024Unbiased Watermark for Large Language Models
168. ICLR 2024Understanding the Effects of RLHF on LLM Generalisation and Diversity
169. ICLR 2024Universal Guidance for Diffusion Models
170. ICLR 2024Unveiling and Manipulating Prompt Influence in Large Language Models
171. ICLR 2024Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis
172. ICLR 2024Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency
173. ICLR 2024WizardCoder: Empowering Code Large Language Models with Evol-Instruct
174. ICLR 2024WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions

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.