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

1,430 papers, page 13 of 29

601. ICML 2026Dimensional Collapse in Transformer Attention Outputs: A Challenge for Sparse Dictionary Learning
602. ICML 2026DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing
603. ICML 2026DiScoFormer: Plug-In Density and Score Estimation with Transformers
604. ICML 2026Distribution Matching Variational AutoEncoder
605. ICML 2026Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models
606. ICML 2026DNACHUNKER: Learnable Tokenization for DNA Language Models
607. ICML 2026Domain Restriction via SAE Multi-Layer Transitions
608. ICML 2026Draft-and-Audit Reinforcement Learning for Optimization Modeling
609. ICML 2026Dual Optimal Transport for Multi-Concept Composition: Structural Alignment and Texture Injection in Diffusion Models
610. ICML 2026DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention
611. ICML 2026DynaMem: Consistent Long Video Generation via Hierarchical Memory and Motion Priors
612. ICML 2026ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios
613. ICML 2026Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers
614. ICML 2026Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation
615. ICML 2026Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing
616. ICML 2026Efficient Diffusion Models via Time Step Optimization with Consistent Training and Inference Constraints
617. ICML 2026Efficient Reasoning with Hidden Thinking
618. ICML 2026Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
619. ICML 2026Efficient Test-time Inference for Generative Planning Models with OCL Search
620. ICML 2026Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization
621. ICML 2026Efficiently Learning Drifting Halfspaces with Massart Noise
622. ICML 2026EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video
623. ICML 2026EigenCache: Rethinking Diffusion Acceleration as Covariance-Optimal Forecasting and Submodular Information Allocation
624. ICML 2026ElicitR: Unlocking Latent Reasoning in Dense Retrievers via Generative Regularization
625. ICML 2026Embedding-perturbed Exploration Preference Optimization for Flow Models
626. ICML 2026End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer
627. ICML 2026EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions
628. ICML 2026Enhancing Cross-subject Emotion Recognition via Heterogeneous Distribution Augmentation and Collaborative Learning
629. ICML 2026Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos
630. ICML 2026Ensembling Sparse Autoencoders
631. ICML 2026Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation
632. ICML 2026Equalized Generative Treatment: Matching f-divergences for Fairness in Generative Models
633. ICML 2026Error Propagation Mechanisms and Compensation Strategies for Quantized Diffusion Models
634. ICML 2026Esoteric Language Models: A Family of Any-Order Diffusion LLMs
635. ICML 2026Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
636. ICML 2026EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization
637. ICML 2026Exploring and Exploiting Stability in Latent Flow Matching
638. ICML 2026Exploring Data-Free LoRA Transferability for Video Diffusion Models
639. ICML 2026Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance
640. ICML 2026Expo-GS: Exposure-Aware Signed Distance Function in Gaussian Splatting for High Dynamic Range
641. ICML 2026Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks
642. ICML 2026Factored Classifier-Free Guidance
643. ICML 2026FaPS: A General and Fast Training Method for Diffusion Models
644. ICML 2026Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
645. ICML 2026Fast and Scalable Analytical Diffusion
646. ICML 2026FAST-AR: Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention
647. ICML 2026FasterVAR: Plug-and-Play Acceleration for Visual Autoregressive Models
648. ICML 2026Feature-Aware (Hyper)graph Generation via Next-Scale Prediction
649. ICML 2026FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
650. ICML 2026FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA

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.