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 17 of 25

801. NeurIPS 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
802. NeurIPS 2023Joint Training of Deep Ensembles Fails Due to Learner Collusion
803. NeurIPS 2023JourneyDB: A Benchmark for Generative Image Understanding
804. NeurIPS 2023KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
805. NeurIPS 2023Katakomba: Tools and Benchmarks for Data-Driven NetHack
806. NeurIPS 2023Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control
807. NeurIPS 2023Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
808. NeurIPS 2023Kiki or Bouba? Sound Symbolism in Vision-and-Language Models
809. NeurIPS 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
810. NeurIPS 2023Label-Only Model Inversion Attacks via Knowledge Transfer
811. NeurIPS 2023Labeling Neural Representations with Inverse Recognition
812. NeurIPS 2023Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset
813. NeurIPS 2023Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
814. NeurIPS 2023LayoutPrompter: Awaken the Design Ability of Large Language Models
815. NeurIPS 2023Learning a 1-layer conditional generative model in total variation
816. NeurIPS 2023Learning and Collusion in Multi-unit Auctions
817. NeurIPS 2023Learning and processing the ordinal information of temporal sequences in recurrent neural circuits
818. NeurIPS 2023Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization
819. NeurIPS 2023Learning Dictionary for Visual Attention
820. NeurIPS 2023Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
821. NeurIPS 2023Learning Energy-based Model via Dual-MCMC Teaching
822. NeurIPS 2023Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
823. NeurIPS 2023Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
824. NeurIPS 2023Learning Exponential Families from Truncated Samples
825. NeurIPS 2023Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
826. NeurIPS 2023Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition
827. NeurIPS 2023Learning Layer-wise Equivariances Automatically using Gradients
828. NeurIPS 2023Learning Mixtures of Gaussians Using the DDPM Objective
829. NeurIPS 2023Learning Modulated Transformation in GANs
830. NeurIPS 2023Learning Multi-agent Behaviors from Distributed and Streaming Demonstrations
831. NeurIPS 2023Learning non-Markovian Decision-Making from State-only Sequences
832. NeurIPS 2023Learning Nonparametric Latent Causal Graphs with Unknown Interventions
833. NeurIPS 2023Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
834. NeurIPS 2023Learning threshold neurons via edge of stability
835. NeurIPS 2023Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
836. NeurIPS 2023Learning to Compress Prompts with Gist Tokens
837. NeurIPS 2023Learning to Configure Separators in Branch-and-Cut
838. NeurIPS 2023Learning To Dive In Branch And Bound
839. NeurIPS 2023Learning to Parameterize Visual Attributes for Open-set Fine-grained Retrieval
840. NeurIPS 2023Learning to Reason and Memorize with Self-Notes
841. NeurIPS 2023Learning to Receive Help: Intervention-Aware Concept Embedding Models
842. NeurIPS 2023Learning to Tokenize for Generative Retrieval
843. NeurIPS 2023Learning Trajectories are Generalization Indicators
844. NeurIPS 2023Learning Transformer Programs
845. NeurIPS 2023Learning Unseen Modality Interaction
846. NeurIPS 2023Learning via Wasserstein-Based High Probability Generalisation Bounds
847. NeurIPS 2023Learning Visual Prior via Generative Pre-Training
848. NeurIPS 2023Learning World Models with Identifiable Factorization
849. NeurIPS 2023LEPARD: Learning Explicit Part Discovery for 3D Articulated Shape Reconstruction
850. NeurIPS 2023Leveraging sparse and shared feature activations for disentangled representation 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.