13 citations · 26 across the 6 of their papers we have counts for
6 papers
FOSI: Hybrid First and Second Order Optimization
Hadar Sivan, Moshe Gabel, Assaf Schuster
Popular machine learning approaches forgo second-order information due to the difficulty of computing curvature in high dimensions. We present FOSI, a novel meta-algorithm that imp…
Optimizing Data Collection in Deep Reinforcement Learning
James Gleeson, Daniel Snider, Yvonne Yang +3
Reinforcement learning (RL) workloads take a notoriously long time to train due to the large number of samples collected at run-time from simulators. Unfortunately, cluster scale-u…
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads
James Gleeson, Srivatsan Krishnan, Moshe Gabel +3
Deep reinforcement learning (RL) has made groundbreaking advancements in robotics, data center management and other applications. Unfortunately, system-level bottlenecks in RL work…
It's Not What Machines Can Learn, It's What We Cannot Teach
Gal Yehuda, Moshe Gabel, Assaf Schuster
Can deep neural networks learn to solve any task, and in particular problems of high complexity? This question attracts a lot of interest, with recent works tackling computationall…
Taming Momentum in a Distributed Asynchronous Environment
Ido Hakimi, Saar Barkai, Moshe Gabel +1
Although distributed computing can significantly reduce the training time of deep neural networks, scaling the training process while maintaining high efficiency and final accuracy…
Wrist02 -- Reliable Peripheral Oxygen Saturation Readings from Wrist-Worn Pulse Oximeters
Caleb Phillips, Daniyal Liaqat, Moshe Gabel +1
Peripheral blood oxygen saturation Sp02 is a vital measure in healthcare. Modern off-the-shelf wrist-worn devices, such as the Apple Watch, FitBit, and Samsung Gear, have an onboar…