15 citations · 38 across the 5 of their papers we have counts for
5 papers
Towards a Personal Health Large Language Model
Justin Cosentino, Anastasiya Belyaeva, Xin Liu +31
In health, most large language model (LLM) research has focused on clinical tasks. However, mobile and wearable devices, which are rarely integrated into such tasks, provide rich,…
Multimodal LLMs for health grounded in individual-specific data
Anastasiya Belyaeva, Justin Cosentino, Farhad Hormozdiari +6
Foundation large language models (LLMs) have shown an impressive ability to solve tasks across a wide range of fields including health. To effectively solve personalized health tas…
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
Babak Alipanahi, Farhad Hormozdiari, Babak Behsaz +10
Genome-wide association studies (GWAS) require accurate cohort phenotyping, but expert labeling can be costly, time-intensive, and variable. Here we develop a machine learning (ML)…
The Search for Sparse, Robust Neural Networks
Justin Cosentino, Federico Zaiter, Dan Pei +1
Recent work on deep neural network pruning has shown there exist sparse subnetworks that achieve equal or improved accuracy, training time, and loss using fewer network parameters…
Generative Well-intentioned Networks
Justin Cosentino, Jun Zhu
We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative networ…