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Utilizing variational autoencoders in the Bayesian Inverse Problem of Photoacoustic Tomography

SIAM JOURNAL ON IMAGING SCIENCES 2023年第1期16卷 89-110页

作者： Sahlstrom, Teemu Tarvainen, Tanja Univ Eastern Finland Dept Appl Phys Kuopio Finland UCL Dept Comp Sci London England

There has been an increasing interest in utilizing machine learning methods in inverse problems and imaging. Most of the work has, however, concentrated on image reconstruction problems, and the number of studies regarding the full solution of the inverse problem is limited. In this work, we study a machine learning--based approach for the Bayesian inverse problem of photoacoustic tomography. We develop an approach for estimating the posterior distribution in photoacoustic tomography using an approach based on the variational autoencoder. The approach is evaluated with numerical simulations and compared to the solution of the inverse problem using a Bayesian approach.

关键词： photoacoustic tomography Bayesian inverse problems variational Bayesian methods machine learning uncertainty quantification variational autoencoder

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Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders

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BMC BIOINFORMATICS 2019年第18期20卷 1-7页

作者： Wang, Zhenxing Wang, Yadong Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China

Background Lung cancer is one of the most malignant tumors, causing over 1,000,000 deaths each year worldwide. Deep learning has brought success in many domains in recent years. DNA methylation, an epigenetic factor, is used for model training in many studies. There is an opportunity for deep learning methods to analyze the lung cancer epigenetic data to determine their subtypes for appropriate treatment. Results Here, we employ variational autoencoders (VAEs), an unsupervised deep learning framework, on 450K DNA methylation data of TCGA-LUAD and TCGA-LUSC to learn latent representations of the DNA methylation landscape. We extract a biologically relevant latent space of LUAD and LUSC samples. It is showed that the bivariate classifiers on the further compressed latent features could classify the subtypes accurately. Through clustering of methylation-based latent space features, we demonstrate that the VAEs can capture differential methylation patterns about subtypes of lung cancer. Conclusions VAEs can distinguish the original subtypes from manually mixed methylation data frame with the encoded features of latent space. Further applications about VAEs should focus on fine-grained subtypes identification for precision medicine.

关键词： DNA methylation Lung cancer variational autoencoder

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Partial Multiple Imputation With variational autoencoders: Tackling Not at Randomness in Healthcare Data

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第8期26卷 4218-4227页

作者： Pereira, Ricardo Cardoso Abreu, Pedro Henriques Rodrigues, Pedro Pereira Univ Coimbra Ctr Informat & Syst Univ Coimbra Dept Informat Engn P-3030290 Coimbra Portugal Univ Porto Fac Med MEDCIDS Ctr Hlth Technol & Serv Res P-4200319 Porto Portugal

Missing data can pose severe consequences in critical contexts, such as clinical research based on routinely collected healthcare data. This issue is usually handled with imputation strategies, but these tend to produce poor and biased results under the Missing Not At Random (MNAR) mechanism. A recent trend that has been showing promising results for MNAR is the use of generative models, particularly variational autoencoders. However, they have a limitation: the imputed values are the result of a single sample, which can be biased. To tackle it, an extension to the variational autoencoder that uses a partial multiple imputation procedure is introduced in this work. The proposed method was compared to 8 state-of-the-art imputation strategies, in an experimental setup with 34 datasets from the medical context, injected with the MNAR mechanism (10% to 80% rates). The results were evaluated through the Mean Absolute Error, with the new method being the overall best in 71% of the datasets, significantly outperforming the remaining ones, particularly for high missing rates. Finally, a case study of a classification task with heart failure data was also conducted, where this method induced improvements in 50% of the classifiers.

关键词： Medical services Task analysis Data models Principal component analysis Neural networks Mathematical models Mice Healthcare data missing data missing not at random partial multiple imputation variational autoencoder

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Aspect-level item recommendation based on user reviews with variational autoencoders

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INFORMATION SCIENCES 2024年 671卷

作者： Ou, Wei Huynh, Van-Nam Zhejiang Gongshang Univ Sch Tourism & Urban Rural Planning Hangzhou Peoples R China Japan Adv Inst Sci & Technol Sch Knowledge Sci Nomi Japan

In this paper we propose an aspect-based recommendation model based on variational autoencoders, that provides not only coarse predictions about what items users may like, but also finer-grained predictions of specific aspects users may be interested in of the recommended items. The proposed model first employs convolution operations to learn probability distributions of aspect-level embeddings for users and items from user aspect-level sentiments on seen items. Then it samples from these distributions and feeds the samples into transpose convolution operations to 'reconstruct' missing aspect-level sentiments for unseen items, thereby serving as signals for generating recommendations. To prevent overfitting, we impose a prior on the representation distributions and penalize the model if the learned distributions diverge from the prior. Based on the output of the proposed model, we propose a two-stage ranking scheme that combines aspect-level and overall sentiment signals to rank items. Experiment results show that the proposed model outperforms state -of -the -art aspect-based recommendation models, and the two-stage ranking scheme improves the traditional ranking by overall sentiment predictions.

关键词： Aspect-based recommender systems variational autoencoder Item ranking Aspect-level sentiment prediction

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Arbitrary conditional inference in variational autoencoders via fast prior network training

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MACHINE LEARNING 2022年第7期111卷 2537-2559页

作者： Wu, Ga Domke, Justin Sanner, Scott Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Univ Massachusetts Coll Comp & Informat Sci Amherst MA 01003 USA

variational autoencoders (VAEs) are a popular generative model, but one in which conditional inference can be challenging. If the decomposition into query and evidence variables is fixed, conditionally trained VAEs provide an attractive solution. However, to efficiently support arbitrary queries over pre-trained VAEs when the query and evidence are not known in advance, one is generally reduced to MCMC sampling methods that can suffer from long mixing times. In this paper, we propose an idea of efficiently training small conditional prior networks to approximate the latent distribution of the VAE after conditioning on an evidence assignment;this permits generating query samples without retraining the full VAE. We experimentally evaluate three variations of conditional prior networks showing that (i) they can be quickly optimized for different decompositions of evidence and query and (ii) they quantitatively and qualitatively outperform existing state-of-the-art methods for conditional inference in pre-trained VAEs.

关键词： variational autoencoder Conditional inference Prior network

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Arbitrage-Free Implied Volatility Surface Generation with variational autoencoders

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SIAM JOURNAL ON FINANCIAL MATHEMATICS 2023年第4期14卷 1004-1027页

作者： Ning, Brian (Xin) Jaimungal, Sebastian Zhang, Xiaorong Bergeron, Maxime Univ Toronto Dept Stat Sci Toronto ON M5G 1Z5 Canada Riskfuel Analyt Toronto ON M5C 1X6 Canada

We propose a hybrid method for generating arbitrage-free implied volatility (IV) surfaces consistent with historical data by combining model-free variational autoencoders (VAEs) with continuous time stochastic differential equation (SDE) driven models. We focus on two classes of SDE models: regime switching models and Le'\vy additive processes. By projecting historical surfaces onto the space of SDE model parameters, we obtain a distribution on the parameter subspace faithful to the data on which we then train a VAE. Arbitrage-free IV surfaces are then generated by sampling from the posterior distribution on the latent space, decoding to obtain SDE model parameters, and finally mapping those parameters to IV surfaces. We further refine the VAE model by including conditional features and demonstrate its superior generative out-of-sample performance. Finally, we showcase how our method can be used as a data augmentation tool to help practitioners manage the tail risk of option portfolios.

关键词： machine learning computational finance implied volatility variational autoencoder Levy processes

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A Survey on variational autoencoders in Recommender Systems

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ACM COMPUTING SURVEYS 2024年第10期56卷 1页

作者： Liang, Shangsong Pan, Zhou Liu, Wei Yin, Jian De Rijke, Maarten Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Artificial Intelligence Zhuhai Peoples R China Univ Amsterdam Amsterdam Noord Holland Netherlands

Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms. To overcome these challenges, various deep learning-based recommendation algorithms have been proposed. Among these, variational autoencoder (VAE)-based recommendation methods stand out. VAEs are based on a flexible probabilistic framework, which is robust for data sparsity and compatible with other deep learning-based models for dealing with multimodal data. In addition, the deep generative structure of VAEs helps to perform Bayesian inference in an efficient manner. VAE-based recommendation algorithms have given rise to many novel graphical models, and they have achieved promising performance. In this article, we conduct a survey to systematically summarize recent VAE-based recommendation algorithms. Four frequently used characteristics of VAE-based recommendation algorithms are summarized, and a taxonomy of VAE-based recommendation algorithms is proposed. We also identify future research directions for, advanced perspectives on, and the application of VAEs in recommendation algorithms, to inspire future work on VAEs for recommender systems.

关键词： variational autoencoder recommender systems deep learning Bayesian network

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A deep learning strategy to identify cell types across species from high-density extracellular recordings

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Cell 2025年第8期188卷 2218-2234.e22页

作者： Beau, Maxime Herzfeld, David J. Naveros, Francisco Hemelt, Marie E. Oostland, Marlies Sánchez-López, Alvaro Chung, Young Yoon Maibach, Michael Kyranakis, Stephen Stabb, Hannah N. Martínez Lopera, M. Gabriela Lajko, Agoston Zedler, Marie Ohmae, Shogo Hall, Nathan J. Clark, Beverley A. Cohen, Dana Lisberger, Stephen G. Kostadinov, Dimitar Hull, Court Häusser, Michael Medina, Javier F. Wolfson Institute for Biomedical Research University College London London United Kingdom Department of Neurobiology Duke University School of Medicine Durham NC United States Department of Neuroscience Baylor College of Medicine Houston TX United States Department of Computer Engineering Automation and Robotics Research Centre for Information and Communication Technologies University of Granada Granada Spain Swammerdam Institute for Life Sciences University of Amsterdam Amsterdam Netherlands The Leslie and Susan Gonda Multidisciplinary Brain Research Center Bar-Ilan University Ramat-Gan Israel Centre for Developmental Neurobiology King's College London London United Kingdom School of Biomedical Sciences The University of Hong Kong Hong Kong China

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals and reveal the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetics and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep learning classifier that predicts cell types with greater than 95% accuracy based on the waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously recorded cell types during behavior. © 2025

关键词： cell-type identification cerebellar cortex cerebellum circuit mapping classification machine learning Neuropixels variational autoencoder

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Multimodal anomaly detection in complex environments using video and audio fusion

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Scientific Reports 2025年第1期15卷 1-22页

作者： Wang, Yuanyuan Zhao, Yijie Huo, Yanhua Lu, Yiping Library Hebei North University Hebei Zhangjiakou 075000 China Educational Technology and Information Center Hebei North University Hebei Zhangjiakou 075000 China

Due to complex environmental conditions and varying noise levels, traditional models are limited in their effectiveness for detecting anomalies in video sequences. Aiming at the challenges of accuracy, robustness, and real-time processing requirements in the field of image and video processing, this study proposes an anomaly detection and recognition algorithm for video image data based on deep learning. The algorithm combines the innovative methods of spatio-temporal feature extraction and noise suppression, and aims to improve the processing performance, especially in complex environments, by introducing an improved Variable Auto Encoder (VAE) structure. The model named Spatio-Temporal Anomaly Detection Network (STADNet) captures the spatio-temporal features of video images through multi-scale Three-Dimensional (3D) convolution module and spatio-temporal attention mechanism. This approach improves the accuracy of anomaly detection. Multi-stream network architecture and cross-attention fusion mechanism are also adopted to comprehensively consider different factors such as color, texture, and motion, and further improve the robustness and generalization ability of the model. The experimental results show that compared with the existing models, the new model has obvious advantages in performance stability and real-time processing under different noise levels. Specifically, the AUC value of the proposed model is 0.95 on UCSD Ped2 dataset, which is about 10% higher than other models, and the AUC value on Avenue dataset is 0.93, which is about 12% higher. This study not only proposes an effective image and video processing scheme but also demonstrates wide practical potential, providing a new perspective and methodological basis for future research and application in related fields. © The Author(s) 2025.

关键词： Deep learning Image processing Spatiotemporal feature extraction variational autoencoder Video processing

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Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 198页

作者： Farhadyar, Kiana Bonofiglio, Federico Hackenberg, Maren Behrens, Max Zoeller, Daniela Binder, Harald Univ Freiburg Inst Med Biometry & Stat Freiburg Germany Univ Freiburg Freiburg Ctr Data Anal & Modeling Freiburg Germany Natl Res Council Italy ISMAR Lerici Italy

In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts.

关键词： Synthetic data Complex distribution Propensity score Deep generative model variational autoencoder

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