1 citations · 1 across the 4 of their papers we have counts for
5 papers
Memory-Efficient Continual Learning with CLIP Models
Ryan King, Gang Li, Bobak Mortazavi +1
Contrastive Language-Image Pretraining (CLIP) models excel at understanding image-text relationships but struggle with adapting to new data without forgetting prior knowledge. To a…
A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability
Ryan King, Conrad Krueger, Ethan Veselka +2
In recent years, machine learning has made significant progress in clinical outcome prediction, demonstrating increasingly accurate results. However, the substantial resources requ…
AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-rays
Chenlang Yi, Zizhan Xiong, Qi Qi +5
Contrastive Language-Image Pre-training (CLIP) models have demonstrated superior performance across various visual tasks including medical image classification. However, fairness c…
An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data
Ryan King, Shivesh Kodali, Conrad Krueger +2
Machine learning has revolutionized the modeling of clinical timeseries data. Using machine learning, a Deep Neural Network (DNN) can be automatically trained to learn a complex ma…
Multimodal Pretraining of Medical Time Series and Notes
Ryan King, Tianbao Yang, Bobak Mortazavi
Within the intensive care unit (ICU), a wealth of patient data, including clinical measurements and clinical notes, is readily available. This data is a valuable resource for compr…