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

776 papers, page 6 of 16

251. NeurIPS 2023Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events
252. NeurIPS 2023Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
253. NeurIPS 2023EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
254. NeurIPS 2023Expert load matters: operating networks at high accuracy and low manual effort
255. NeurIPS 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
256. NeurIPS 2023Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
257. NeurIPS 2023Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
258. NeurIPS 2023Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
259. NeurIPS 2023Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
260. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning
261. NeurIPS 2023FABind: Fast and Accurate Protein-Ligand Binding
262. NeurIPS 2023Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
263. NeurIPS 2023FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
264. NeurIPS 2023Facing Off World Model Backbones: RNNs, Transformers, and S4
265. NeurIPS 2023Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
266. NeurIPS 2023Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
267. NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
268. NeurIPS 2023Fast Attention Requires Bounded Entries
269. NeurIPS 2023Fast Optimal Locally Private Mean Estimation via Random Projections
270. NeurIPS 2023Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees
271. NeurIPS 2023Fast Trainable Projection for Robust Fine-tuning
272. NeurIPS 2023Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
273. NeurIPS 2023Feature Learning for Interpretable, Performant Decision Trees
274. NeurIPS 2023Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
275. NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
276. NeurIPS 2023Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
277. NeurIPS 2023Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds
278. NeurIPS 2023Federated Learning with Manifold Regularization and Normalized Update Reaggregation
279. NeurIPS 2023Federated Spectral Clustering via Secure Similarity Reconstruction
280. NeurIPS 2023Fine-Tuning Language Models with Just Forward Passes
281. NeurIPS 2023FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
282. NeurIPS 2023First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
283. NeurIPS 2023Flow Factorized Representation Learning
284. NeurIPS 2023Flow: Per-instance Personalized Federated Learning
285. NeurIPS 2023Formulating Discrete Probability Flow Through Optimal Transport
286. NeurIPS 2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
287. NeurIPS 2023Full-Atom Protein Pocket Design via Iterative Refinement
288. NeurIPS 2023Functional Renyi Differential Privacy for Generative Modeling
289. NeurIPS 2023Fundamental Limits and Tradeoffs in Invariant Representation Learning
290. NeurIPS 2023Game Solving with Online Fine-Tuning
291. NeurIPS 2023Gaussian Mixture Solvers for Diffusion Models
292. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
293. NeurIPS 2023Generalized Information-theoretic Multi-view Clustering
294. NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
295. NeurIPS 2023Generalizing Nonlinear ICA Beyond Structural Sparsity
296. NeurIPS 2023Generating QM1B with PySCF$_{\text{IPU}}$
297. NeurIPS 2023Generative Category-level Object Pose Estimation via Diffusion Models
298. NeurIPS 2023Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
299. NeurIPS 2023Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
300. NeurIPS 2023Generative Neural Fields by Mixtures of Neural Implicit Functions

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.