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 29 of 35

1401. NeurIPS 2023Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features
1402. NeurIPS 2023Neural Sampling in Hierarchical Exponential-family Energy-based Models
1403. NeurIPS 2023NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
1404. NeurIPS 2023Neuro-symbolic Learning Yielding Logical Constraints
1405. NeurIPS 2023NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries
1406. NeurIPS 2023No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
1407. NeurIPS 2023No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
1408. NeurIPS 2023Noether Embedding: Efficient Learning of Temporal Regularities
1409. NeurIPS 2023Non-adversarial training of Neural SDEs with signature kernel scores
1410. NeurIPS 2023Non-Rigid Shape Registration via Deep Functional Maps Prior
1411. NeurIPS 2023Nonparametric Boundary Geometry in Physics Informed Deep Learning
1412. NeurIPS 2023Normalization-Equivariant Neural Networks with Application to Image Denoising
1413. NeurIPS 2023NPCL: Neural Processes for Uncertainty-Aware Continual Learning
1414. NeurIPS 2023Object-centric Learning with Cyclic Walks between Parts and Whole
1415. NeurIPS 2023Object-Centric Slot Diffusion
1416. NeurIPS 2023Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage
1417. NeurIPS 2023On Calibrating Diffusion Probabilistic Models
1418. NeurIPS 2023On Learning Latent Models with Multi-Instance Weak Supervision
1419. NeurIPS 2023On Measuring Fairness in Generative Models
1420. NeurIPS 2023On Proper Learnability between Average- and Worst-case Robustness
1421. NeurIPS 2023On quantum backpropagation, information reuse, and cheating measurement collapse
1422. NeurIPS 2023On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
1423. NeurIPS 2023On Single-Index Models beyond Gaussian Data
1424. NeurIPS 2023On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
1425. NeurIPS 2023On the Constrained Time-Series Generation Problem
1426. NeurIPS 2023On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration
1427. NeurIPS 2023On the impact of activation and normalization in obtaining isometric embeddings at initialization
1428. NeurIPS 2023On the Role of Entanglement and Statistics in Learning
1429. NeurIPS 2023On the Role of Randomization in Adversarially Robust Classification
1430. NeurIPS 2023On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm
1431. NeurIPS 2023On the Variance, Admissibility, and Stability of Empirical Risk Minimization
1432. NeurIPS 2023On-the-Fly Adapting Code Summarization on Trainable Cost-Effective Language Models
1433. NeurIPS 2023One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
1434. NeurIPS 2023One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning
1435. NeurIPS 2023One-Step Diffusion Distillation via Deep Equilibrium Models
1436. NeurIPS 2023OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
1437. NeurIPS 2023Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost
1438. NeurIPS 2023Online Clustering of Bandits with Misspecified User Models
1439. NeurIPS 2023OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
1440. NeurIPS 2023Optimal cross-learning for contextual bandits with unknown context distributions
1441. NeurIPS 2023Optimal Transport Model Distributional Robustness
1442. NeurIPS 2023Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
1443. NeurIPS 2023Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
1444. NeurIPS 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
1445. NeurIPS 2023Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
1446. NeurIPS 2023PAC Learning Linear Thresholds from Label Proportions
1447. NeurIPS 2023PaintSeg: Painting Pixels for Training-free Segmentation
1448. NeurIPS 2023Pairwise GUI Dataset Construction Between Android Phones and Tablets
1449. NeurIPS 2023Parallel Sampling of Diffusion Models
1450. NeurIPS 2023Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models

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.