98 citations · 180 across the 14 of their papers we have counts for
14 papers
Pyreal: A Framework for Interpretable ML Explanations
Alexandra Zytek, Wei-En Wang, Dongyu Liu +2
Users in many domains use machine learning (ML) predictions to help them make decisions. Effective ML-based decision-making often requires explanations of ML models and their predi…
Beep: Balancing Effectiveness and Efficiency when Finding Multivariate Patterns in Racket Sports
Jiang Wu, Dongyu Liu, Ziyang Guo +1
Modeling each hit as a multivariate event in racket sports and conducting sequential analysis aids in assessing player/team performance and identifying successful tactics for coach…
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…
RASIPAM: Interactive Pattern Mining of Multivariate Event Sequences in Racket Sports
Jiang Wu, Dongyu Liu, Ziyang Guo +1
Experts in racket sports like tennis and badminton use tactical analysis to gain insight into competitors' playing styles. Many data-driven methods apply pattern mining to racket s…
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…
The Need for Interpretable Features: Motivation and Taxonomy
Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu +2
Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their…
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…
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Furui Cheng, Dongyu Liu, Fan Du +5
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks. Although many ML models perform promisingly, issues with model…
AQEyes: Visual Analytics for Anomaly Detection and Examination of Air Quality Data
Dongyu Liu, Kalyan Veeramachaneni, Alexander Geiger +2
Anomaly detection plays a key role in air quality analysis by enhancing situational awareness and alerting users to potential hazards. However, existing anomaly detection approache…
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making
Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan +1
Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts - who often have no expertise in ML or data science - are asked t…
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…
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
Qianwen Wang, Yao Ming, Zhihua Jin +5
To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically s…
DeepTracker: Visualizing the Training Process of Convolutional Neural Networks
Dongyu Liu, Weiwei Cui, Kai Jin +2
Deep convolutional neural networks (CNNs) have achieved remarkable success in various fields. However, training an excellent CNN is practically a trial-and-error process that consu…