21 citations · 32 across the 11 of their papers we have counts for
12 papers
Harnessing Vision-Language Models for Time Series Anomaly Detection
Zelin He, Sarah Alnegheimish, Matthew Reimherr
Time-series anomaly detection (TSAD) has played a vital role in a variety of fields, including healthcare, finance, and sensor-based condition monitoring. Prior methods, which main…
MAD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding
Sarah Alnegheimish, Zelin He, Matthew Reimherr +3
With the widespread availability of sensor data across industrial and operational systems, we frequently encounter heterogeneous time series from multiple systems. Anomaly detectio…
Explingo: Explaining AI Predictions using Large Language Models
Alexandra Zytek, Sara Pido, Sarah Alnegheimish +2
Explanations of machine learning (ML) model predictions generated by Explainable AI (XAI) techniques such as SHAP are essential for people using ML outputs for decision-making. We…
Large language models can be zero-shot anomaly detectors for time series?
Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille +1
Recent studies have shown the ability of large language models to perform a variety of tasks, including time series forecasting. The flexible nature of these models allows them to…
Single Word Change is All You Need: Using LLMs to Create Synthetic Training Examples for Text Classifiers
Lei Xu, Sarah Alnegheimish, Laure Berti-Equille +2
In text classification, creating an adversarial example means subtly perturbing a few words in a sentence without changing its meaning, causing it to be misclassified by a classifi…
OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User
Sarah Alnegheimish, Laure Berti-Equille, Kalyan Veeramachaneni
Time series anomaly detection is a vital task in many domains, including patient monitoring in healthcare, forecasting in finance, and predictive maintenance in energy industries.…
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
Lawrence Wong, Dongyu Liu, Laure Berti-Equille +2
Anomaly detection on time series data is increasingly common across various industrial domains that monitor metrics in order to prevent potential accidents and economic losses. How…
Sintel: A Machine Learning Framework to Extract Insights from Signals
Sarah Alnegheimish, Dongyu Liu, Carles Sala +2
The detection of anomalies in time series data is a critical task with many monitoring applications. Existing systems often fail to encompass an end-to-end detection process, to fa…
MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series
Dongyu Liu, Sarah Alnegheimish, Alexandra Zytek +1
Detecting anomalies in time-varying multivariate data is crucial in various industries for the predictive maintenance of equipment. Numerous machine learning (ML) algorithms have b…
Probabilistic Programming Bots in Intuitive Physics Game Play
Fahad Alhasoun, Sarah Alnegheimish, Joshua Tenenbaum
Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the physics of objects. We propose a framework for bots to deploy probabili…
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
Sarah Alnegheimish, Najat Alrashed, Faisal Aleissa +4
An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted an…
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
Alexander Geiger, Dongyu Liu, Sarah Alnegheimish +2
Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, det…