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

286 papers, page 6 of 6

251. ICML 2023Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
252. ICML 2023State and parameter learning with PARIS particle Gibbs
253. ICML 2023Statistical Inference and A/B Testing for First-Price Pacing Equilibria
254. ICML 2023STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
255. ICML 2023Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
256. ICML 2023Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning
257. ICML 2023Target-Aware Generative Augmentations for Single-Shot Adaptation
258. ICML 2023Test-time Adaptation with Slot-Centric Models
259. ICML 2023TGRL: An Algorithm for Teacher Guided Reinforcement Learning
260. ICML 2023The Acquisition of Physical Knowledge in Generative Neural Networks
261. ICML 2023The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
262. ICML 2023The Catalog Problem: Clustering and Ordering Variable-Sized Sets
263. ICML 2023The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
264. ICML 2023The SSL Interplay: Augmentations, Inductive Bias, and Generalization
265. ICML 2023The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
266. ICML 2023Thompson Sampling with Diffusion Generative Prior
267. ICML 2023Tighter Bounds on the Expressivity of Transformer Encoders
268. ICML 2023Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models
269. ICML 2023Towards Deep Attention in Graph Neural Networks: Problems and Remedies
270. ICML 2023Towards Sustainable Learning: Coresets for Data-efficient Deep Learning
271. ICML 2023Towards Understanding and Improving GFlowNet Training
272. ICML 2023Transformers as Algorithms: Generalization and Stability in In-context Learning
273. ICML 2023Unconstrained Online Learning with Unbounded Losses
274. ICML 2023Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective
275. ICML 2023Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits
276. ICML 2023Unlocking Slot Attention by Changing Optimal Transport Costs
277. ICML 2023Unscented Autoencoder
278. ICML 2023Unveiling the Latent Space Geometry of Push-Forward Generative Models
279. ICML 2023Variational Mixture of HyperGenerators for Learning Distributions over Functions
280. ICML 2023Vector Quantized Wasserstein Auto-Encoder
281. ICML 2023Weakly Supervised Disentangled Generative Causal Representation Learning
282. ICML 2023When is Realizability Sufficient for Off-Policy Reinforcement Learning?
283. ICML 2023When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
284. ICML 2023Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
285. ICML 2023Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?
286. ICML 2023Width and Depth Limits Commute in Residual Networks

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.