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 11 of 16

501. NeurIPS 2023OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
502. NeurIPS 2023Optimal cross-learning for contextual bandits with unknown context distributions
503. NeurIPS 2023Optimal Transport Model Distributional Robustness
504. NeurIPS 2023Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
505. NeurIPS 2023Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
506. NeurIPS 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
507. NeurIPS 2023Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
508. NeurIPS 2023PAC Learning Linear Thresholds from Label Proportions
509. NeurIPS 2023PaintSeg: Painting Pixels for Training-free Segmentation
510. NeurIPS 2023Pairwise GUI Dataset Construction Between Android Phones and Tablets
511. NeurIPS 2023Parallel Sampling of Diffusion Models
512. NeurIPS 2023Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models
513. NeurIPS 2023Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition
514. NeurIPS 2023Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck
515. NeurIPS 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
516. NeurIPS 2023Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage
517. NeurIPS 2023Passive learning of active causal strategies in agents and language models
518. NeurIPS 2023Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks
519. NeurIPS 2023Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games
520. NeurIPS 2023Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
521. NeurIPS 2023Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
522. NeurIPS 2023Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability
523. NeurIPS 2023Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width
524. NeurIPS 2023PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
525. NeurIPS 2023PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
526. NeurIPS 2023Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication
527. NeurIPS 2023PoET: A generative model of protein families as sequences-of-sequences
528. NeurIPS 2023PointGPT: Auto-regressively Generative Pre-training from Point Clouds
529. NeurIPS 2023PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
530. NeurIPS 2023Polyhedron Attention Module: Learning Adaptive-order Interactions
531. NeurIPS 2023Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
532. NeurIPS 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
533. NeurIPS 2023Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
534. NeurIPS 2023Predicting a Protein's Stability under a Million Mutations
535. NeurIPS 2023PreDiff: Precipitation Nowcasting with Latent Diffusion Models
536. NeurIPS 2023Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
537. NeurIPS 2023Private estimation algorithms for stochastic block models and mixture models
538. NeurIPS 2023Probabilistic Invariant Learning with Randomized Linear Classifiers
539. NeurIPS 2023PrObeD: Proactive Object Detection Wrapper
540. NeurIPS 2023Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
541. NeurIPS 2023Promises and Pitfalls of Threshold-based Auto-labeling
542. NeurIPS 2023Prompt-augmented Temporal Point Process for Streaming Event Sequence
543. NeurIPS 2023Propagating Knowledge Updates to LMs Through Distillation
544. NeurIPS 2023ProPILE: Probing Privacy Leakage in Large Language Models
545. NeurIPS 2023Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
546. NeurIPS 2023ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
547. NeurIPS 2023ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics
548. NeurIPS 2023ProteinShake: Building datasets and benchmarks for deep learning on protein structures
549. NeurIPS 2023Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior
550. NeurIPS 2023Provable Guarantees for Neural Networks via Gradient Feature Learning

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.