27 citations · 41 across the 9 of their papers we have counts for
10 papers
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
Yanbo Xu, Alind Khare, Glenn Matlin +4
Machine Learning (ML) research has focused on maximizing the accuracy of predictive tasks. ML models, however, are increasingly more complex, resource intensive, and costlier to de…
Automatic Parallelization of Python Programs for Distributed Heterogeneous Computing
Jun Shirako, Akihiro Hayashi, Sri Raj Paul +2
This paper introduces a novel approach to automatic ahead-of-time (AOT) parallelization and optimization of sequential Python programs for execution on distributed heterogeneous pl…
CompOFA: Compound Once-For-All Networks for Faster Multi-Platform Deployment
Manas Sahni, Shreya Varshini, Alind Khare +1
The emergence of CNNs in mainstream deployment has necessitated methods to design and train efficient architectures tailored to maximize the accuracy under diverse hardware & laten…
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units
Shenda Hong, Yanbo Xu, Alind Khare +5
Deep learning models have achieved expert-level performance in healthcare with an exclusive focus on training accurate models. However, in many clinical environments such as intens…
HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline
Richard Liaw, Romil Bhardwaj, Lisa Dunlap +4
Prior research in resource scheduling for machine learning training workloads has largely focused on minimizing job completion times. Commonly, these model training workloads colle…
Cloudburst: Stateful Functions-as-a-Service
Vikram Sreekanti, Chenggang Wu, Xiayue Charles Lin +5
Function-as-a-Service (FaaS) platforms and "serverless" cloud computing are becoming increasingly popular. Current FaaS offerings are targeted at stateless functions that do minima…