5 citations · 14 across the 7 of their papers we have counts for
7 papers
AQuA: A Benchmarking Tool for Label Quality Assessment
Mononito Goswami, Vedant Sanil, Arjun Choudhry +3
Machine learning (ML) models are only as good as the data they are trained on. But recent studies have found datasets widely used to train and evaluate ML models, e.g. ImageNet, to…
Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstruction
Cecilia Morales, Jason Yao, Tejas Rane +3
Robot-guided catheter insertion has the potential to deliver urgent medical care in situations where medical personnel are unavailable. However, this technique requires accurate an…
Classifying Unstructured Clinical Notes via Automatic Weak Supervision
Chufan Gao, Mononito Goswami, Jieshi Chen +1
Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. Due to the unstructured nature of th…
The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling
Brian Kunzer, Mario Berges, Artur Dubrawski
The concept of a digital twin has exploded in popularity over the past decade, yet confusion around its plurality of definitions, its novelty as a new technology, and its practical…
Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact
Arnab Dey, Mononito Goswami, Joo Heung Yoon +4
A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To co…
Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
Maria De-Arteaga, Alexandra Chouldechova, Artur Dubrawski
Effective human-AI collaboration requires a system design that provides humans with meaningful ways to make sense of and critically evaluate algorithmic recommendations. In this pa…
Generative Modeling Helps Weak Supervision (and Vice Versa)
Benedikt Boecking, Nicholas Roberts, Willie Neiswanger +3
Many promising applications of supervised machine learning face hurdles in the acquisition of labeled data in sufficient quantity and quality, creating an expensive bottleneck. To…