Variational Autoencoders

Also known as VAE

Variational autoencoders learn probability distributions over compact latent codes and decode samples from them, typically by balancing reconstruction quality against a KL-divergence regularizer in the evidence lower bound. Common approaches use Gaussian, mixture, flow-based, spherical, or heavy-tailed priors, and add hierarchical, disentangling, temporal, multimodal, geometric, or physics-informed structure. They are used for image, video, audio, molecule, medical, and scientific data, both as generative models and as compact representations for downstream prediction or diffusion. A central tension is that stronger regularization can make generation easier while causing posterior collapse or losing useful detail; reconstruction scores alone also do not guarantee good samples, calibrated uncertainty, or useful latent structure.

Latest papers

210 papers30 papers in the last four weeks, up 131% on the four weeks before. 0.2% of all new papers.

Time-Aware Latent Space Bayesian Optimization
Mar 1, 2026Tuan A. Vu, Julien Martinelli, Harri LähdesmäkiMolecule Design Bayesian Optimization
Aalto University
Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencoder
Feb 28, 2026Dennis M. J. van de Sande, Julian P. Merkofer, Sina Amirrajab +4Magnetic Resonance Spectroscopy Synthetic Data Generation
Department of Biomedical Engineering Eindhoven University of Technology Eindhoven, The Netherlands · Department of Electrical Engineering Eindhoven University of Technology Eindhoven, The Netherlands · Department of Radiology & Nuclear Medicine Maastricht University Medical Center Maastricht, The Netherlands
Latent Matters: Learning Deep State-Space Models
Feb 26, 2026Alexej Klushyn, Richard Kurle, Maximilian Soelch +2State Space Models Latent Dynamics Modeling
Technical University of Munich · AWS AI Labs · Machine Learning Research Lab, Volkswagen Group, Munich +1
Disentangled Hierarchical VAE for 3D Human-Human Interaction Generation
Feb 24, 2026Zichen Geng, Zeeshan Hayder, Bo Miao +3Human Motion Generation Variational Autoencoders
Department of CSSE, The University of Western Australia, Crawley WA 6009, Australia · Data61, CSIRO, Acton ACT 2601, Australia · Australian Institute for Machine Learning, The University of Adelaide, Adelaide SA 5000, Australia +1
VAE-MS: An Asymmetric Variational Autoencoder for Mutational Signature Extraction
Feb 24, 2026Ida Egendal, Rasmus Froberg Brøndum, Dan J Woodcock +2Cancer Genomics Variational Autoencoders
Center for Clinical Data Science, Aalborg University and Aalborg University Hospital, Aalborg, 9260, Denmark. · Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, 9000, Denmark. · Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX3 9DU, England. +1
Unsupervised Anomaly Detection in NSL-KDD Using $β$ -VAE: A Latent Space and Reconstruction Error Approach
Feb 23, 2026Dylan Baptiste, Ramla Saddem, Alexandre Philippot +1Unsupervised Anomaly Detection Network Intrusion Detection
CRESTIC · Université de Reims Champagne-Ardenne, CRESTIC, Reims, France · Seckiot, Paris, France
Flash-VAED: Plug-and-Play VAE Decoders for Efficient Video Generation
Feb 22, 2026Lunjie Zhu, Yushi Huang, Xingtong Ge +5Channel Pruning Variational Autoencoders
iComAI Lab, Hong Kong University of Science and Technology, Hong Kong SAR, China · School of Data Science, Lingnan University, Hong Kong SAR, China
PCA-VAE: Differentiable Subspace Quantization without Codebook Collapse
Feb 21, 2026Hao Lu, Onur C. Koyun, Yongxin Guo +3Vector Quantization Vector-Quantized Variational Autoencoder
Wake Forest University School of Medicine, Winston-Salem, NC, USA
Variational Distributional Neuron
Feb 20, 2026Yves RuffenachVariational Autoencoders Variational Inference
Conservatoire National des Arts et Métiers
HiVAE: Hierarchical Latent Variables for Scalable Theory of Mind
Feb 18, 2026Nigel Doering, Rahath Malladi, Arshia Sangwan +2Theory of Mind Cognitive Architectures for AI Agents
University of California San Diego, School of Computing, Information, and Data Sciences · New York University · University of Virginia
Cryo-SWAN: the Multi-Scale Wavelet-decomposition-inspired Autoencoder Network for molecular density representation of molecular volumes
Feb 18, 2026Rui Li, Artsemi Yushkevich, Mikhail Kudryashev +13D Representation Learning Molecular Representation Learning
Center for Advanced Systems Understanding (CASUS), Görlitz, Germany. · Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR), Dresden, Germany. · In situ Structural Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany. +4
Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)
Feb 3, 2026Alex Finkelstein, Ron Moneta, Or Zohar +4Magnetic Resonance Imaging Uncertainty Quantification
School of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel · Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel · School of Biochemistry, Neurobiology, and Biophysics, Tel-Aviv University, Tel-Aviv, Israel +1
Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification
Feb 3, 2026Jack Michael Solomon, Rishi Leburu, Matthias ChungVariational Autoencoders Uncertainty Quantification
Emory University Department of Mathematics
XFACTORS: Disentangled Information Bottleneck via Contrastive Supervision
Jan 29, 2026Alexandre Myara, Nicolas Bourriez, Thomas Boyer +3Variational Autoencoders Disentangled Representation Learning
ENS Paris
VENI: Variational Encoder for Natural Illumination
Jan 20, 2026Paul Walker, James A. D. Gardner, Andreea Ardelean +2Variational Autoencoders Inverse Rendering
Friedrich-Alexander-Universität Erlangen-Nürnberg · University of York
QSAR-Guided Generative Framework for the Discovery of Synthetically Viable Odorants
Dec 28, 2025Tim C. Pearce, Ahmed IbrahimMolecular Generation Variational Autoencoders
Biomedical Engineering Research Group, School of Engineering, University of Leicester, University Road, Leicester LE1 7RH, United Kingdom · School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0SP, United Kingdom
Distribution Matching Variational AutoEncoder
Dec 8, 2025Sen Ye, Jianning Pei, Mengde Xu +4Variational Autoencoders Generative Modeling
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · University of Chinese Academy of Sciences · Tencent +1
Latent Nonlinear Denoising Score Matching for Enhanced Learning of Structured Distributions
Dec 7, 2025Kaichen Shen, Wei ZhuScore-Based Generative Modeling Diffusion Models
School of Mathematics, Georgia Institute of Technology, Atlanta, GA
Delving into Latent Spectral Biasing of Video VAEs for Superior Diffusability
Dec 5, 2025Shizhan Liu, Xinran Deng, Zhuoyi Yang +3Variational Autoencoders Spectral Regularization
Zhipu AI, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Tsinghua University, Beijing, China
Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization
Oct 17, 2025Luo Long, Coralia Cartis, Paz Fink ShustinVariational Autoencoders Bayesian Optimization
Mathematical Institute, University of Oxford, Radcliffe Observatory, Andrew Wiles Building, Woodstock Rd, Oxford, OX2 6GG, United Kingdom.
Latent Representation Learning from 3D Brain MRI for Latent Analysis and Prediction in Multiple Sclerosis
Sep 28, 2025Trinh Ngoc Huynh, Nguyen Duc Kien, Nguyen Hai Anh +8Medical Image Representation Learning Variational Autoencoders
VNU University of Engineering and Technology, Hanoi, Vietnam. · Bach Mai Hospital, Hanoi, Vietnam. · Department of Radiology, First Faculty of Medicine, Charles University, General University Hospital, Prague, Czech Republic. +3
DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy
Aug 16, 2025Frederik L. Dennig, Daniel A. KeimVariational Autoencoders Uncertainty Quantification
University of Konstanz
L-GTA: Latent Generative Modeling for Time Series Augmentation
Jul 31, 2025Luis Roque, Vitor Cerqueira, Carlos Soares +1Variational Autoencoders Synthetic Data Augmentation
Faculdade de Engenharia da Universidade do Porto, Porto, Portugal · University of Coimbra, Coimbra, Portugal · Fraunhofer Portugal AICOS, Portugal +1
Spherical Cauchy Variational Autoencoders: Heavy Angular Tails and Exact KL Evaluation
Jun 26, 2025Lukas Sablica, Kurt HornikVariational Autoencoders Hyperspherical Representation Learning
Institute for Statistics and Mathematics Vienna University of Economics and Business Austria
Cross-Layer Discrete Concept Discovery for Interpreting Language Models
Jun 24, 2025Ankur Garg, Xuemin Yu, Hassan Sajjad +1Variational Autoencoders LLM Interpretability
Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification
Jun 23, 2025Ciro Listone, Aniello MuranoVariational Autoencoders Skin Lesion Classification
University of Naples Federico II, Naples, Italy.
Variational Learning of Disentangled Representations
Jun 20, 2025Yuli Slavutsky, Ozgur Beker, David Blei +1Variational Autoencoders Disentangled Representation Learning
Department of Statistics, Columbia University, New York City, USA · Irving Institute for Cancer Dynamics, Columbia University, New York City, USA · Department of Computer Science, Columbia University, New York City, USA +1
Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis
Jun 1, 2025Qi Chen, Jierui Zhu, Florian ShkurtiVariational Autoencoders Neural Network Generalization
Department of Computer Science, University of Toronto · Data Science Institute · Vector Institute +2
Autoencoders in Function Space
Aug 2, 2024Justin Bunker, Mark Girolami, Hefin Lambley +2Variational Autoencoders Autoencoders
Department of Engineering University of Cambridge Cambridge, CB2 1TN, United Kingdom · Department of Engineering, University of Cambridge · and Alan Turing Institute +3
Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization
Apr 20, 2022Changjie Lu, Shen Zheng, Gaurav GuptaUnsupervised Domain Adaptation Medical Image Segmentation
College of Science and Technology, Wenzhou-Kean University, Wenzhou, China

Mar 1, 2026J/K move · Enter open · S save

Time-Aware Latent Space Bayesian Optimization

Tuan A. Vu, Julien Martinelli, Harri Lähdesmäki

Aalto University

Molecule Design Bayesian Optimization Latent Space Optimization Variational Autoencoders

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Latent-space Bayesian optimization (LSBO) extends Bayesian optimization to structured domains, such as molecular design, by searching in the continuous latent space of a generative model. However, most LSBO methods assume a fixed objective, whereas real design campaigns often face temporal drift (e.g., evolving preferences or shifting targets). Bringing time-varying BO into LSBO is nontrivial: drift can affect not only the surrogate, but also the latent search space geometry induced by the representation. We propose Time-Aware Latent-space Bayesian Optimization (TALBO), which incorporates time in both the surrogate and the learned generative representation via a GP-prior variational autoencoder, yielding a latent space aligned as objectives evolve. To evaluate timevarying LSBO systematically, we adapt widely used molecular design tasks to drifting multi-property objectives and introduce metrics tailored to changing targets. Across these benchmarks, TALBO consistently outperforms strong LSBO baselines and remains robust across drift speeds and design choices, while remaining competitive under actually time-invariant objectives.