732 citations · 800 across the 10 of their papers we have counts for
13 papers · 1 filter
Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events
Matthew B. A. McDermott, Bret Nestor, Peniel Argaw +1
Generative, pre-trained transformers (GPTs, a.k.a. "Foundation Models") have reshaped natural language processing (NLP) through their versatility in diverse downstream tasks. Howev…
Using Machine Learning to Develop Smart Reflex Testing Protocols
Matthew McDermott, Anand Dighe, Peter Szolovits +2
Objective: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Refle…
Meta-Learning to Improve Pre-Training
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith +2
Pre-training (PT) followed by fine-tuning (FT) is an effective method for training neural networks, and has led to significant performance improvements in many domains. PT can inco…
ML4H Abstract Track 2020
Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck +3
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020. This index is not complete, as some accepted abstracts chose to opt-out…
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data
Matthew B. A. McDermott, Bret Nestor, Evan Kim +4
Multi-task learning (MTL) is a machine learning technique aiming to improve model performance by leveraging information across many tasks. It has been used extensively on various d…
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output
Matthew B. A. McDermott, Tzu Ming Harry Hsu, Wei-Hung Weng +2
It is often infeasible or impossible to obtain ground truth labels for medical data. To circumvent this, one may build rule-based or other expert-knowledge driven labelers to inges…