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

4,314 papers, page 5 of 87

201. ICLR 2026Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis
202. ICLR 2026Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space
203. ICLR 2026Beyond Scattered Acceptance: Fast and Coherent Inference for DLMs via Longest Stable Prefixes
204. ICLR 2026Bi-Lipschitz Autoencoder With Injectivity Guarantee
205. ICLR 2026Bidirectional Predictive Coding
206. ICLR 2026BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Images
207. ICLR 2026BranchGRPO: Stable and Efficient GRPO with Structured Branching in Diffusion Models
208. ICLR 2026Break the Trade-off Between Watermark Strength and Speculative Sampling Efficiency for Language Models
209. ICLR 2026Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models
210. ICLR 2026Bridging ML and algorithms: comparison of hyperbolic embeddings
211. ICLR 2026CARE: Covariance-Aware and Rank-Enhanced Decomposition for Enabling Multi-Head Latent Attention
212. ICLR 2026Carré du champ flow matching: better quality-generalisation tradeoff in generative models
213. ICLR 2026Characterizing Deep Research: A Benchmark and Formal Definition
214. ICLR 2026Choices Speak Louder than Questions
215. ICLR 2026CMT: Mid-Training for Efficient Learning of Consistency, Mean Flow, and Flow-Map Models
216. ICLR 2026CO3: CONTRASTING CONCEPTS COMPOSE BETTER
217. ICLR 2026Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting
218. ICLR 2026ConfHit: Conformal Generative Design via Nested Testing
219. ICLR 2026Confident and Adaptive Generative Speech Recognition via Conformal Risk Control
220. ICLR 2026Contact Wasserstein Geodesics for Non-Conservative Schrödinger Bridges
221. ICLR 2026Continual Low-Rank Adapters for LLM-based Generative Recommender Systems
222. ICLR 2026DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learning
223. ICLR 2026Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield
224. ICLR 2026Decoupled MeanFlow: Turning Flow Models into Flow Maps for Accelerated Sampling
225. ICLR 2026DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence
226. ICLR 2026DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights
227. ICLR 2026Delay Flow Matching
228. ICLR 2026DeMo: Decoupled Momentum Optimization
229. ICLR 2026Detecting and Mitigating Memorization in Diffusion Models through Anisotropy of the Log-Probability
230. ICLR 2026Diagnosing and Improving Diffusion Models by Estimating Optimal Loss Value
231. ICLR 2026DiCache: Let Diffusion Model Determine Its Own Cache
232. ICLR 2026DiffSDA: Unsupervised Diffusion Sequential Disentanglement Across Modalities
233. ICLR 2026Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting
234. ICLR 2026Diffusion Language Model Knows the Answer Before It Decodes
235. ICLR 2026Diffusion Transformers with Representation Autoencoders
236. ICLR 2026Discovering and Steering Interpretable Concepts in Large Generative Music Models
237. ICLR 2026Disentanglement of Variations with Multimodal Generative Modeling
238. ICLR 2026Distributionally Robust Optimization via Generative Ambiguity Modeling
239. ICLR 2026Diversity-Aware Online Prompt Assignment to Generative Models
240. ICLR 2026Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs
241. ICLR 2026Does Higher Interpretability Imply Better Utility? A Pairwise Analysis on Sparse Autoencoders
242. ICLR 2026DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
243. ICLR 2026dParallel: Learnable Parallel Decoding for dLLMs
244. ICLR 2026Dual-Solver: A Generalized ODE Solver for Diffusion Models with Dual Prediction
245. ICLR 2026DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies
246. ICLR 2026EchoMotion: Unified Human Video and Motion Generation via Dual-Modality Diffusion Transformer
247. ICLR 2026EgoTwin: Dreaming Body and View in First Person
248. ICLR 2026Eliminating VAE for Fast and High-Resolution Generative Detail Restoration
249. ICLR 2026Energy-Based Transformers are Scalable Learners and Thinkers
250. ICLR 2026Enhanced Generative Model Evaluation with Clipped Density and Coverage

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.