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 16 of 25

751. NeurIPS 2023Graph Contrastive Learning with Stable and Scalable Spectral Encoding
752. NeurIPS 2023Grassmann Manifold Flows for Stable Shape Generation
753. NeurIPS 2023Guiding The Last Layer in Federated Learning with Pre-Trained Models
754. NeurIPS 2023H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets
755. NeurIPS 2023H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
756. NeurIPS 2023Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
757. NeurIPS 2023Harnessing Hard Mixed Samples with Decoupled Regularizer
758. NeurIPS 2023HeadSculpt: Crafting 3D Head Avatars with Text
759. NeurIPS 2023Hierarchical clustering with dot products recovers hidden tree structure
760. NeurIPS 2023Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning
761. NeurIPS 2023High-dimensional Asymptotics of Denoising Autoencoders
762. NeurIPS 2023High-dimensional Contextual Bandit Problem without Sparsity
763. NeurIPS 2023How a Student becomes a Teacher: learning and forgetting through Spectral methods
764. NeurIPS 2023How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
765. NeurIPS 2023How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget
766. NeurIPS 2023How to Turn Your Knowledge Graph Embeddings into Generative Models
767. NeurIPS 2023HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection
768. NeurIPS 2023Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
769. NeurIPS 2023HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models
770. NeurIPS 2023ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
771. NeurIPS 2023Identification of Nonlinear Latent Hierarchical Models
772. NeurIPS 2023Im-Promptu: In-Context Composition from Image Prompts
773. NeurIPS 2023ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
774. NeurIPS 2023Imitation Learning from Vague Feedback
775. NeurIPS 2023Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data
776. NeurIPS 2023Implicit Variational Inference for High-Dimensional Posteriors
777. NeurIPS 2023IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI
778. NeurIPS 2023Improved Frequency Estimation Algorithms with and without Predictions
779. NeurIPS 2023Improving Adversarial Transferability via Intermediate-level Perturbation Decay
780. NeurIPS 2023Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings
781. NeurIPS 2023Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data
782. NeurIPS 2023Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
783. NeurIPS 2023In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer
784. NeurIPS 2023In-Context Learning Unlocked for Diffusion Models
785. NeurIPS 2023Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization
786. NeurIPS 2023Incentivized Communication for Federated Bandits
787. NeurIPS 2023Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
788. NeurIPS 2023Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training
789. NeurIPS 2023Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes
790. NeurIPS 2023Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction
791. NeurIPS 2023Invariant Learning via Probability of Sufficient and Necessary Causes
792. NeurIPS 2023Inverse Preference Learning: Preference-based RL without a Reward Function
793. NeurIPS 2023Is Distance Matrix Enough for Geometric Deep Learning?
794. NeurIPS 2023iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
795. NeurIPS 2023Isometric Quotient Variational Auto-Encoders for Structure-Preserving Representation Learning
796. NeurIPS 2023Iterative Reachability Estimation for Safe Reinforcement Learning
797. NeurIPS 2023Iteratively Learn Diverse Strategies with State Distance Information
798. NeurIPS 2023Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels
799. NeurIPS 2023Jigsaw: Learning to Assemble Multiple Fractured Objects
800. NeurIPS 2023Joint Attribute and Model Generalization Learning for Privacy-Preserving Action Recognition

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.