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 13 of 16

601. NeurIPS 2023Robust Learning with Progressive Data Expansion Against Spurious Correlation
602. NeurIPS 2023Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
603. NeurIPS 2023SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise
604. NeurIPS 2023Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling
605. NeurIPS 2023Sample Complexity of Forecast Aggregation
606. NeurIPS 2023Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
607. NeurIPS 2023Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
608. NeurIPS 2023Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
609. NeurIPS 2023SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery
610. NeurIPS 2023SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning
611. NeurIPS 2023Scalarization for Multi-Task and Multi-Domain Learning at Scale
612. NeurIPS 2023Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
613. NeurIPS 2023Scaling Laws for Hyperparameter Optimization
614. NeurIPS 2023Scaling MLPs: A Tale of Inductive Bias
615. NeurIPS 2023Scaling Riemannian Diffusion Models
616. NeurIPS 2023Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion
617. NeurIPS 2023Schema-learning and rebinding as mechanisms of in-context learning and emergence
618. NeurIPS 2023Score-based Generative Modeling through Stochastic Evolution Equations in Hilbert Spaces
619. NeurIPS 2023Score-based Generative Models with Lévy Processes
620. NeurIPS 2023Score-based Source Separation with Applications to Digital Communication Signals
621. NeurIPS 2023SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models
622. NeurIPS 2023Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
623. NeurIPS 2023Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
624. NeurIPS 2023Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
625. NeurIPS 2023Semantic Image Synthesis with Unconditional Generator
626. NeurIPS 2023Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
627. NeurIPS 2023Semi-Implicit Denoising Diffusion Models (SIDDMs)
628. NeurIPS 2023Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation
629. NeurIPS 2023Semi-Supervised Domain Generalization with Known and Unknown Classes
630. NeurIPS 2023Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots
631. NeurIPS 2023Siamese Masked Autoencoders
632. NeurIPS 2023SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
633. NeurIPS 2023SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
634. NeurIPS 2023Simple and Asymmetric Graph Contrastive Learning without Augmentations
635. NeurIPS 2023Simple and Controllable Music Generation
636. NeurIPS 2023Simple, Scalable and Effective Clustering via One-Dimensional Projections
637. NeurIPS 2023SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
638. NeurIPS 2023Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions
639. NeurIPS 2023Skill-it! A data-driven skills framework for understanding and training language models
640. NeurIPS 2023SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization
641. NeurIPS 2023SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models
642. NeurIPS 2023Slow and Weak Attractor Computation Embedded in Fast and Strong E-I Balanced Neural Dynamics
643. NeurIPS 2023Smooth, exact rotational symmetrization for deep learning on point clouds
644. NeurIPS 2023Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
645. NeurIPS 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
646. NeurIPS 2023Sorting with Predictions
647. NeurIPS 2023Sparse Deep Learning for Time Series Data: Theory and Applications
648. NeurIPS 2023Sparse Parameterization for Epitomic Dataset Distillation
649. NeurIPS 2023Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning
650. NeurIPS 2023Spectral Co-Distillation for Personalized Federated 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.