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

1,714 papers, page 28 of 35

1351. NeurIPS 2023LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion
1352. NeurIPS 2023List and Certificate Complexities in Replicable Learning
1353. NeurIPS 2023LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing
1354. NeurIPS 2023LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition
1355. NeurIPS 2023Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning
1356. NeurIPS 2023Loss Decoupling for Task-Agnostic Continual Learning
1357. NeurIPS 2023Loss Dynamics of Temporal Difference Reinforcement Learning
1358. NeurIPS 2023Lossy Image Compression with Conditional Diffusion Models
1359. NeurIPS 2023Lung250M-4B: A Combined 3D Dataset for CT- and Point Cloud-Based Intra-Patient Lung Registration
1360. NeurIPS 2023Markovian Sliced Wasserstein Distances: Beyond Independent Projections
1361. NeurIPS 2023Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
1362. NeurIPS 2023Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
1363. NeurIPS 2023Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits
1364. NeurIPS 2023Maximum State Entropy Exploration using Predecessor and Successor Representations
1365. NeurIPS 2023May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
1366. NeurIPS 2023MedSat: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery
1367. NeurIPS 2023Meek Separators and Their Applications in Targeted Causal Discovery
1368. NeurIPS 2023Meet in the Middle: A New Pre-training Paradigm
1369. NeurIPS 2023Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning
1370. NeurIPS 2023Meta-Learning with Neural Bandit Scheduler
1371. NeurIPS 2023Metis: Understanding and Enhancing In-Network Regular Expressions
1372. NeurIPS 2023Metropolis Sampling for Constrained Diffusion Models
1373. NeurIPS 2023MGDD: A Meta Generator for Fast Dataset Distillation
1374. NeurIPS 2023Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
1375. NeurIPS 2023Minimum Description Length and Generalization Guarantees for Representation Learning
1376. NeurIPS 2023Mirror Diffusion Models for Constrained and Watermarked Generation
1377. NeurIPS 2023Mitigating Test-Time Bias for Fair Image Retrieval
1378. NeurIPS 2023Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
1379. NeurIPS 2023MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
1380. NeurIPS 2023Mnemosyne: Learning to Train Transformers with Transformers
1381. NeurIPS 2023Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
1382. NeurIPS 2023Model Spider: Learning to Rank Pre-Trained Models Efficiently
1383. NeurIPS 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
1384. NeurIPS 2023Model-enhanced Vector Index
1385. NeurIPS 2023Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
1386. NeurIPS 2023Module-wise Training of Neural Networks via the Minimizing Movement Scheme
1387. NeurIPS 2023Multi Time Scale World Models
1388. NeurIPS 2023Multi-Object Representation Learning via Feature Connectivity and Object-Centric Regularization
1389. NeurIPS 2023Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
1390. NeurIPS 2023Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
1391. NeurIPS 2023MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks
1392. NeurIPS 2023NAP: Neural 3D Articulated Object Prior
1393. NeurIPS 2023NAS-X: Neural Adaptive Smoothing via Twisting
1394. NeurIPS 2023NCDL: A Framework for Deep Learning on non-Cartesian Lattices
1395. NeurIPS 2023Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
1396. NeurIPS 2023Near-optimal learning with average Hölder smoothness
1397. NeurIPS 2023Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
1398. NeurIPS 2023Neural Image Compression: Generalization, Robustness, and Spectral Biases
1399. NeurIPS 2023Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem
1400. NeurIPS 2023Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability

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.