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

275 papers, page 5 of 6

201. ICML 2025REG: Rectified Gradient Guidance for Conditional Diffusion Models
202. ICML 2025REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective
203. ICML 2025Rényi Neural Processes
204. ICML 2025RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
205. ICML 2025Rethinking Aleatoric and Epistemic Uncertainty
206. ICML 2025Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
207. ICML 2025Revisiting Non-Acyclic GFlowNets in Discrete Environments
208. ICML 2025Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
209. ICML 2025RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
210. ICML 2025Robust Multi-bit Text Watermark with LLM-based Paraphrasers
211. ICML 2025Robust Secure Swap: Responsible Face Swap With Persons of Interest Redaction and Provenance Traceability
212. ICML 2025RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression
213. ICML 2025S4S: Solving for a Fast Diffusion Model Solver
214. ICML 2025SADA: Stability-guided Adaptive Diffusion Acceleration
215. ICML 2025SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability
216. ICML 2025SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
217. ICML 2025SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer
218. ICML 2025SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion
219. ICML 2025Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment
220. ICML 2025Scaling Laws for Pre-training Agents and World Models
221. ICML 2025Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows
222. ICML 2025Score-of-Mixture Training: One-Step Generative Model Training Made Simple via Score Estimation of Mixture Distributions
223. ICML 2025Selective Response Strategies for GenAI
224. ICML 2025Self-Consuming Generative Models with Adversarially Curated Data
225. ICML 2025Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency
226. ICML 2025Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation
227. ICML 2025Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
228. ICML 2025SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation
229. ICML 2025Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation
230. ICML 2025Smooth Interpolation for Improved Discrete Graph Generative Models
231. ICML 2025SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation
232. ICML 2025Sparse Autoencoders, Again?
233. ICML 2025Spatial Reasoning with Denoising Models
234. ICML 2025Spherical-Nested Diffusion Model for Panoramic Image Outpainting
235. ICML 2025SSHR: More Secure Generative Steganography with High-Quality Revealed Secret Images
236. ICML 2025Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness
237. ICML 2025Stochastic Forward–Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets
238. ICML 2025Symmetry-Aware GFlowNets
239. ICML 2025TabNAT: A Continuous-Discrete Joint Generative Framework for Tabular Data
240. ICML 2025TabSDS: a Lightweight, Fully Non-Parametric, and Model Free Approach for Generating Synthetic Tabular Data
241. ICML 2025Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion
242. ICML 2025Teaching Language Models to Critique via Reinforcement Learning
243. ICML 2025Test-Time Graph Neural Dataset Search With Generative Projection
244. ICML 2025Testing Conditional Mean Independence Using Generative Neural Networks
245. ICML 2025The Diffusion Duality
246. ICML 2025TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision
247. ICML 2025ToMA: Token Merge with Attention for Diffusion Models
248. ICML 2025Towards a Mechanistic Explanation of Diffusion Model Generalization
249. ICML 2025Towards a Unified Framework of Clustering-based Anomaly Detection
250. ICML 2025Tractable Transformers for Flexible Conditional Generation

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.