Mr. Brian Baracat-Donovan Profile

Brian Baracat-Donovan

at Noblis Inc

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Proceedings Article | 17 October 2023 Paper

Swiss Army VAE: comprehensive sensor data analysis via explainable AI

John Helmsen, Brian Baracat-Donovan, Riley White, Thomas Cullough

Proceedings Volume 12742, 127420I (2023) https://doi.org/10.1117/12.2680317

KEYWORDS: Data modeling, Sensors, Artificial intelligence, Data analysis, Army, Statistical modeling, Evolutionary algorithms, Education and training, Data processing, Classification systems

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An integrated system for processing sensor data has been developed based on novel variational autoencoder (VAE) algorithms with explainability that significantly eases analysis of sensor data. By continuously updating a generative model of the data, the system assists users with minimal artificial intelligence (AI) training or experience to perform data analysis. The system performs an extensive range of integrated machine learning (ML) tasks: anomaly detection, active learning, model-drift detection, synthetic data generation, semi-supervised classification, and counterfactual explanation generation. When the system is provided a data schema (map of Booleans, integers, reals, categories, time series, etc.) and data set, it automatically forms a preliminary generative model of the data. The construction of the system is modular, so new data types can be added as necessary. Counterfactually explainable anomaly detection is immediately performed via sparse gradient search. This informs the user how to interactively remove or repair bad records and/or begin labeling records of interest. The addition of labels to the data allows multi-class, semi-supervised, counterfactually explainable classification via the support vector machine embedded hyperplane algorithm (SVM-EH). Once some labels are added, active learning is used to assist further labeling by suggesting data elements that are highly likely to improve classification accuracy, significantly accelerating the labeling process by trading human effort for computational cycles. In production, the system detects when its training is becoming stale and requests retraining.

Proceedings Article | 17 October 2023 Presentation + Paper

New methods for explainable variational autoencoders

Riley White, Brian Baracat-Donovan, John Helmsen, Thomas McCullough

Proceedings Volume 12742, 127420J (2023) https://doi.org/10.1117/12.2680290

KEYWORDS: Education and training, Neural networks, Machine learning, Deep learning, Data conversion, Data modeling, Telecommunications, Support vector machines, Polonium, Neodymium

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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.

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