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Multilevel Anomaly Detection Through variational autoencoders and Bayesian Models for Self-Aware Embodied Agents

IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 1399-1414页

作者： Slavic, Giulia Baydoun, Mohamad Campo, Damian Marcenaro, Lucio Regazzoni, Carlo Univ Genoa DITEN Fac Engn I-16145 Genoa Italy Univ Genoa DITEN I-16145 Genoa Italy

Anomaly detection constitutes a fundamental step in developing self-aware autonomous agents capable of continuously learning from new situations, as it enables to distinguish novel experiences from already encountered ones. This paper combines Dynamic Bayesian Networks (DBNs) and Neural Networks (NNs) and proposes a method for detecting anomalies in video data at different abstraction levels. We use a variational autoencoder (VAE) to reduce the dimensionality of video frames, and Optical Flows between subsequent images, generating a latent space that captures both visual and dynamical information and that is comparable to low-dimensional sensory data (e.g., positioning, steering angle). An Adapted Markov Jump Particle Filter is employed to predict the following frames and detect anomalies in video data. Our method's evaluation is executed using different video data from a semi-autonomous vehicle performing different tasks in a closed environment. Tests on benchmark anomaly detection datasets have additionally been conducted.

关键词： Feature extraction Anomaly detection Predictive models Image reconstruction Self-aware Probabilistic logic Data models Anomaly detection kalman filtering particle filtering variational autoencoder

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Using Shapley Values and variational autoencoders to Explain Predictive Models with Dependent Mixed Features

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JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-51页

作者： Olsen, Lars Henry Berge Glad, Ingrid Kristine Jullum, Martin Aas, Kjersti Univ Oslo Dept Math Moltke Moes vei 35Niels Henrik Abels hus N-0851 Oslo Norway Norwegian Comp Ctr Gaustadalleen 23aKristen Nygaards hus N-0373 Oslo Norway

Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoret-ical properties and a sound mathematical foundation in the field of cooperative game theory. Precise Shapley value estimates for dependent data rely on accurate modeling of the depen-dencies between all feature combinations. In this paper, we use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We demonstrate through comprehensive simulation studies that our VAEAC approach to Shapley value estimation outperforms the state-of-the-art methods for a wide range of settings for both continuous and mixed dependent features. For high-dimensional settings, our VAEAC approach with a non-uniform masking scheme significantly outperforms competing meth-ods. Finally, we apply our VAEAC approach to estimate Shapley value explanations for the Abalone data set from the UCI Machine Learning Repository.

关键词： Explainable artificial intelligence prediction explanation Shapley values variational autoencoder feature dependence

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A process monitoring and fault isolation framework based on variational autoencoders and branch and bound method

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JOURNAL OF THE FRANKLIN INSTITUTE 2022年第2期359卷 1667-1691页

作者： Tang, Peng Peng, Kaixiang Jiao, Ruihua Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Peng Cheng Lab Dept Math & Theories 2 Xingke 1st St Nanshan Shenzhen Peoples R China AVIC Xian Aviat Brake Technol Co Ltd Xian 710065 Peoples R China

Nonlinear characteristic widely exists in industrial processes. Many approaches based on kernel methods and machine learning have been developed for nonlinear process monitoring. However, the fault isolation for nonlinear processes has rarely been studied in previous works. In this paper, a process monitoring and fault isolation framework is proposed for nonlinear processes using variational autoen-coder (VAE) model. First, based on the probability graph model of VAE, a uniform monitoring index can be calculated by the probability density of observation variables. Then, the fault variables are estimated with normal variables by a missing value estimation method. The optimal fault variable set can be searched by branch and bound (BAB) algorithm. The proposed method can resolve the "smearing effects" problem existing in traditional fault isolation methods. Finally, a numerical case and a hot strip mill process case are used to verified the proposed method. (C) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Process monitoring Fault isolation variational autoencoder Missing value estimation Branch and bound

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Disease Progression Score Estimation From Multimodal Imaging and MicroRNA Data Using Supervised variational autoencoders

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第12期26卷 6024-6035页

作者： Kmetzsch, Virgilio Becker, Emmanuelle Saracino, Dario Rinaldi, Daisy Camuzat, Agnes Le Ber, Isabelle Colliot, Olivier Sorbonne Univ Paris Brain Inst ICM AP HPCNRSInriaInsermInst Cerveau F-75013 Paris France Univ Rennes Inria CNRS IRISA F-35000 Rennes France Sorbonne Univ Paris Brain Inst ICM AP HPInst CerveauCNRSInserm F-75013 Paris France Sorbonne Univ Paris Brain Inst Hop Pitie Salpetriere AP HPInst Memory & Alzheimers Dis IM2AInst Cerv F-75013 Paris France

Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microRNA) data have been shown useful in tracking their progression. However, no single biomarker can accurately measure progression in these complex diseases. Additionally, large samples are not available for such rare disorders. It is thus essential to develop methods that can model disease progression by combining multiple biomarkers from small samples. In this paper, we propose a new framework for computing a disease progression score (DPS) from cross-sectional multimodal data. Specifically, we introduce a supervised multimodal variational autoencoder that can infer a meaningful latent space, where latent representations are placed along a disease trajectory. A score is computed by orthogonal projections onto this path. We evaluate our framework with multiple synthetic datasets and with a real dataset containing 14 patients, 40 presymptomatic genetic mutation carriers and 37 controls from the PREV-DEMALS study. There is no ground truth for the DPS in real-world scenarios, therefore we use the area under the ROC curve (AUC) as a proxy metric. Results with the synthetic datasets support this choice, since the higher the AUC, the more accurate the predicted simulated DPS. Experiments with the real dataset demonstrate better performance in comparison with state-of-the-art approaches. The proposed framework thus leverages cross-sectional multimodal datasets with small sample sizes to objectively measure disease progression, with potential application in clinical trials.

关键词： Disease progression score deep learning microRNA multimodal data neurodegenerative disease neuroimaging variational autoencoder

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New Methods for Explainable variational autoencoders

New Methods for Explainable Variational Autoencoders

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Conference on Artificial Intelligence for Security and Defence Applications

作者： White, Riley Baracat-Donovan, Brian Helmsen, John McCullough, Thomas Noblis Inc 2022 Edmund Halley Dr Reston VA 20191 USA

ISBN: (纸本)9781510667136;9781510667143

A new deep learning algorithm for performing anomaly detection and multi-class classification with explainability using counterfactuals is described. The system is a variational autoencoder (VAE) with a modified loss function and new methods for counterfactual identification. An additional hinge-loss term is added to VAE training. This enables convenient synthetic data generation and allows straightforward construction of multi-class counterfactuals. Counterfactuals are synthetic data generated to explain system decisions by answering the question: "If this data was not anomalous or was in another class, what modifications would need to be made?" To determine counterfactuals, a path is determined through the embedding space via adversarial attack-like techniques to minimize reconstruction error, with the restriction of minimally altering the number of columns changed. Large changes are allowed, unlike adversarial attack approaches, so changes are isolated and easily visible. Anomaly detection is performed by modifying a result to lower its anomalousness. Classification changes are performed by modifying the data to another class. Multi-class classification is performed on the embedding space of the VAE via an attached linear support vector machine (SVM). By adding the hinge-loss term to the VAE embedding training as well as the SVM, the embedding is modified to prefer class separation without being informed of the specific class labels. This causes the classes in the embedding space to be separated by hyperplanes, making counterfactual generation convenient and SVM classification accurate. Accuracy is shown to be comparable to other deep learners. Approaches to accommodating the image and time-series data are discussed.

关键词： Deep Learning Anomaly Detection Explainability variational autoencoder

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Generalizing Across Domains in Diabetic Retinopathy via variational autoencoders 26th

Generalizing Across Domains in Diabetic Retinopathy via Vari...

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26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Chokuwa, Sharon Khan, Muhammad H. Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031474002;9783031474019

Domain generalization for Diabetic Retinopathy (DR) classification allows a model to adeptly classify retinal images from previously unseen domains with various imaging conditions and patient demographics, thereby enhancing its applicability in a wide range of clinical environments. In this study, we explore the inherent capacity of variational autoencoders to disentangle the latent space of fundus images, with an aim to obtain a more robust and adaptable domain-invariant representation that effectively tackles the domain shift encountered in DR datasets. Despite the simplicity of our approach, we explore the efficacy of this classical method and demonstrate its ability to outperform contemporary state-of-the-art approaches for this task using publicly available datasets. Our findings challenge the prevailing assumption that highly sophisticated methods for DR classification are inherently superior for domain generalization. This highlights the importance of considering simple methods and adapting them to the challenging task of generalizing medical images, rather than solely relying on advanced techniques.

关键词： Domain Generalization Diabetic Retinopathy 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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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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Pair-variational autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques

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JACS AU 2023年第9期3卷 2510-2521页

作者： Lu, Shizhao Jayaraman, Arthi Univ Delaware Dept Chem & Biomol Engn Newark DE 19716 USA Univ Delaware Dept Mat Sci & Engn Newark DE 19716 USA

In materials research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of a synthesized material. Depending on the availability and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other technique(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small-angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques (easy and difficult to interpret, fast and slow in data collection or sample preparation) so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, Pair-variational autoencoders (PairVAE), that works with data from small-angle X-ray scattering (SAXS) that present information about bulk morphology and images from scanning electron microscopy (SEM) that present two-dimensional local structural information on the sample. Using paired SAXS and SEM data of newly observed block copolymer assembled morphologies [open access data from ], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern and vice versa. This method can be extended to other soft material morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as an engine for generating ensembles of similar microscopy images to create a database for other downstream calculations of structure-property relationships.

关键词： machine learning material characterization scattering microscopy variational autoencoder

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Towards Data-Driven Volatility Modeling with variational autoencoders 1

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Dierckx, Thomas Davis, Jesse Schoutens, Wim Katholieke Univ Leuven Dept Stat & Risk Leuven Belgium Katholieke Univ Leuven Dept Comp Sci Leuven Belgium

ISBN: (数字)9783031236334

ISBN: (纸本)9783031236327;9783031236334

In this study, we show how S&P 500 Index volatility surfaces can be modeled in a purely data-driven way using variational autoencoders. The approach autonomously learns concepts such as the volatility level, smile, and term structure without leaning on hypotheses from traditional volatility modeling techniques. In addition to introducing notable improvements to an existing variational autoencoder approach for the reconstruction of both complete and incomplete volatility surfaces, we showcase three practical use cases to highlight the relevance of this approach to the financial industry. First, we show how the latent space learned by the variational autoencoder can be used to produce synthetic yet realistic volatility surfaces. Second, we demonstrate how entire sequences of synthetic volatility surfaces can be generated to stress test and analyze an options portfolio. Third and last, we detect anomalous surfaces in our options dataset and pinpoint exactly which subareas are divergent.

关键词： Volatility modeling variational autoencoder Synthetic data Market simulation Anomaly detection Portfolio stress testing

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