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

1,304 papers, page 21 of 27

1001. ICML 2024Stacking Deep Set Networks and Pooling by Quantiles
1002. ICML 2024Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
1003. ICML 2024Stochastic Interpolants with Data-Dependent Couplings
1004. ICML 2024StrWAEs to Invariant Representations
1005. ICML 2024Switched Flow Matching: Eliminating Singularities via Switching ODEs
1006. ICML 2024The Emergence of Reproducibility and Consistency in Diffusion Models
1007. ICML 2024The Good, The Bad, and Why: Unveiling Emotions in Generative AI
1008. ICML 2024The Linear Representation Hypothesis and the Geometry of Large Language Models
1009. ICML 2024The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright BreachesWithout Adjusting Finetuning Pipeline
1010. ICML 2024Timer: Generative Pre-trained Transformers Are Large Time Series Models
1011. ICML 2024Topological Neural Networks go Persistent, Equivariant, and Continuous
1012. ICML 2024Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components
1013. ICML 2024Towards Neural Architecture Search through Hierarchical Generative Modeling
1014. ICML 2024Towards Scalable and Versatile Weight Space Learning
1015. ICML 2024Towards Theoretical Understandings of Self-Consuming Generative Models
1016. ICML 2024Transformers, parallel computation, and logarithmic depth
1017. ICML 2024Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
1018. ICML 2024Why do Variational Autoencoders Really Promote Disentanglement?
1019. ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
1020. ICML 2023A Flexible Diffusion Model
1021. ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
1022. ICML 2023A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
1023. ICML 2023A Mathematical Model for Curriculum Learning for Parities
1024. ICML 2023A Modern Look at the Relationship between Sharpness and Generalization
1025. ICML 2023A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
1026. ICML 2023A Statistical Perspective on Retrieval-Based Models
1027. ICML 2023A theory of continuous generative flow networks
1028. ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
1029. ICML 2023abess: A Fast Best-Subset Selection Library in Python and R
1030. ICML 2023Action Matching: Learning Stochastic Dynamics from Samples
1031. ICML 2023AdaBoost is not an Optimal Weak to Strong Learner
1032. ICML 2023AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
1033. ICML 2023Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
1034. ICML 2023Adaptive IMLE for Few-shot Pretraining-free Generative Modelling
1035. ICML 2023Adversarial Cheap Talk
1036. ICML 2023Adversarial Learning of Distributional Reinforcement Learning
1037. ICML 2023An SDE for Modeling SAM: Theory and Insights
1038. ICML 2023Analyzing Diffusion as Serial Reproduction
1039. ICML 2023Attributing Image Generative Models using Latent Fingerprints
1040. ICML 2023Bayes-optimal Learning of Deep Random Networks of Extensive-width
1041. ICML 2023Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts
1042. ICML 2023Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
1043. ICML 2023Better Diffusion Models Further Improve Adversarial Training
1044. ICML 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectories
1045. ICML 2023BiBench: Benchmarking and Analyzing Network Binarization
1046. ICML 2023Bidirectional Learning for Offline Model-based Biological Sequence Design
1047. ICML 2023Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers
1048. ICML 2023Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
1049. ICML 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
1050. ICML 2023Brauer's Group Equivariant Neural Networks

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.