550 research outputs found
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source
Significant advances in utilizing deep learning for anomaly detection have
been made in recent years. However, these methods largely assume the existence
of a normal training set (i.e., uncontaminated by anomalies) or even a
completely labeled training set. In many complex engineering systems, such as
particle accelerators, labels are sparse and expensive; in order to perform
anomaly detection in these cases, we must drop these assumptions and utilize a
completely unsupervised method. This paper introduces the Resilient Variational
Autoencoder (ResVAE), a deep generative model specifically designed for anomaly
detection. ResVAE exhibits resilience to anomalies present in the training data
and provides feature-level anomaly attribution. During the training process,
ResVAE learns the anomaly probability for each sample as well as each
individual feature, utilizing these probabilities to effectively disregard
anomalous examples in the training data. We apply our proposed method to detect
anomalies in the accelerator status at the SLAC Linac Coherent Light Source
(LCLS). By utilizing shot-to-shot data from the beam position monitoring
system, we demonstrate the exceptional capability of ResVAE in identifying
various types of anomalies that are visible in the accelerator
Recent Advances in Anomaly Detection Methods Applied to Aviation
International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data
Recent advances in Explainable AI (XAI) increased the demand for deployment
of safe and interpretable AI models in various industry sectors. Despite the
latest success of deep neural networks in a variety of domains, understanding
the decision-making process of such complex models still remains a challenging
task for domain experts. Especially in the financial domain, merely pointing to
an anomaly composed of often hundreds of mixed type columns, has limited value
for experts. Hence, in this paper, we propose a framework for explaining
anomalies using denoising autoencoders designed for mixed type tabular data. We
specifically focus our technique on anomalies that are erroneous observations.
This is achieved by localizing individual sample columns (cells) with potential
errors and assigning corresponding confidence scores. In addition, the model
provides the expected cell value estimates to fix the errors. We evaluate our
approach based on three standard public tabular datasets (Credit Default,
Adult, IEEE Fraud) and one proprietary dataset (Holdings). We find that
denoising autoencoders applied to this task already outperform other approaches
in the cell error detection rates as well as in the expected value rates.
Additionally, we analyze how a specialized loss designed for cell error
detection can further improve these metrics. Our framework is designed for a
domain expert to understand abnormal characteristics of an anomaly, as well as
to improve in-house data quality management processes.Comment: 10 pages, 4 figures, 3 tables, preprint versio
Hierarchical Bayesian Data Fusion Using Autoencoders
In this thesis, a novel method for tracker fusion is proposed and evaluated for vision-based tracking. This work combines three distinct popular techniques into a recursive Bayesian estimation algorithm. First, semi supervised learning approaches are used to partition data and to train a deep neural network that is capable of capturing normal visual tracking operation and is able to detect anomalous data. We compare various methods by examining their respective receiver operating conditions (ROC) curves, which represent the trade off between specificity and sensitivity for various detection threshold levels. Next, we incorporate the trained neural networks into an existing data fusion algorithm to replace its observation weighing mechanism, which is based on the Mahalanobis distance. We evaluate different semi-supervised learning architectures to determine which is the best for our problem. We evaluated the proposed algorithm on the OTB-50 benchmark dataset and compared its performance to the performance of the constituent trackers as well as with previous fusion. Future work involving this proposed method is to be incorporated into an autonomous following unmanned aerial vehicle (UAV)
Anomaly Awareness
We present a new Machine Learning algorithm called Anomaly Awareness. By
making our algorithm aware of the presence of a range of different anomalies,
we improve its capability to detect anomalous events, even those it had not
been exposed to. As an example of use, we apply this method to searches for new
phenomena in the Large Hadron Collider. In particular, we analyze events with
boosted jets where new physics could be hiding.Comment: 8 pages, 11 figure
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