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

1,216 papers, page 11 of 25

501. NeurIPS 2023An Iterative Self-Learning Framework for Medical Domain Generalization
502. NeurIPS 2023Anchor Data Augmentation
503. NeurIPS 2023Anonymous and Copy-Robust Delegations for Liquid Democracy
504. NeurIPS 2023Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization
505. NeurIPS 2023Anytime-Competitive Reinforcement Learning with Policy Prior
506. NeurIPS 2023Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
507. NeurIPS 2023ASPEN: Breaking Operator Barriers for Efficient Parallelization of Deep Neural Networks
508. NeurIPS 2023Attention as Implicit Structural Inference
509. NeurIPS 2023Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
510. NeurIPS 2023Augmenting Language Models with Long-Term Memory
511. NeurIPS 2023Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
512. NeurIPS 2023Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings
513. NeurIPS 2023Balancing Risk and Reward: A Batched-Bandit Strategy for Automated Phased Release
514. NeurIPS 2023Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance
515. NeurIPS 2023Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
516. NeurIPS 2023Benchmark of Machine Learning Force Fields for Semiconductor Simulations: Datasets, Metrics, and Comparative Analysis
517. NeurIPS 2023Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase
518. NeurIPS 2023Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Bone Shape Reconstruction
519. NeurIPS 2023Beta Diffusion
520. NeurIPS 2023Better Correlation and Robustness: A Distribution-Balanced Self-Supervised Learning Framework for Automatic Dialogue Evaluation
521. NeurIPS 2023Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
522. NeurIPS 2023Beyond Confidence: Reliable Models Should Also Consider Atypicality
523. NeurIPS 2023Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends
524. NeurIPS 2023Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time
525. NeurIPS 2023Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm
526. NeurIPS 2023BiMatting: Efficient Video Matting via Binarization
527. NeurIPS 2023BIOT: Biosignal Transformer for Cross-data Learning in the Wild
528. NeurIPS 2023Birth of a Transformer: A Memory Viewpoint
529. NeurIPS 2023Bottleneck Structure in Learned Features: Low-Dimension vs Regularity Tradeoff
530. NeurIPS 2023Boundary Guided Learning-Free Semantic Control with Diffusion Models
531. NeurIPS 2023Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models
532. NeurIPS 2023Bridging Discrete and Backpropagation: Straight-Through and Beyond
533. NeurIPS 2023Bridging RL Theory and Practice with the Effective Horizon
534. NeurIPS 2023Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models
535. NeurIPS 2023BubbleML: A Multiphase Multiphysics Dataset and Benchmarks for Machine Learning
536. NeurIPS 2023Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark
537. NeurIPS 2023Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes
538. NeurIPS 2023C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
539. NeurIPS 2023Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
540. NeurIPS 2023Canonical normalizing flows for manifold learning
541. NeurIPS 2023CAT-Walk: Inductive Hypergraph Learning via Set Walks
542. NeurIPS 2023CBD: A Certified Backdoor Detector Based on Local Dominant Probability
543. NeurIPS 2023Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback
544. NeurIPS 2023Certification of Distributional Individual Fairness
545. NeurIPS 2023Certified Minimax Unlearning with Generalization Rates and Deletion Capacity
546. NeurIPS 2023Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
547. NeurIPS 2023CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
548. NeurIPS 2023Chanakya: Learning Runtime Decisions for Adaptive Real-Time Perception
549. NeurIPS 2023Change point detection and inference in multivariate non-parametric models under mixing conditions
550. NeurIPS 2023Characterization and Learning of Causal Graphs with Small Conditioning Sets

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.