191 citations · 224 across the 6 of their papers we have counts for
8 papers
GenoML: Automated Machine Learning for Genomics
Mary B. Makarious, Hampton L. Leonard, Dan Vitale +14
GenoML is a Python package automating machine learning workflows for genomics (genetics and multi-omics) with an open science philosophy. Genomics data require significant domain e…
Caramel: Accelerating Decentralized Distributed Deep Learning with Computation Scheduling
Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Brighten Godfrey +1
The method of choice for parameter aggregation in Deep Neural Network (DNN) training, a network-intensive task, is shifting from the Parameter Server model to decentralized aggrega…
R-Storm: Resource-Aware Scheduling in Storm
Boyang Peng, Mohammad Hosseini, Zhihao Hong +2
The era of big data has led to the emergence of new systems for real-time distributed stream processing, e.g., Apache Storm is one of the most popular stream processing systems in…
Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data
Vipul Satone, Rachneet Kaur, Faraz Faghri +3
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are chara…
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy H. Campbell
State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communicatio…
Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case
Faraz Faghri, Sayed Hadi Hashemi, Mohammad Babaeizadeh +3
In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most…