571 citations · 1.8k across the 19 of their papers we have counts for
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On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal +2
The stochastic gradient descent (SGD) method and its variants are algorithms of choice for many Deep Learning tasks. These methods operate in a small-batch regime wherein a fractio…
Distributed Hessian-Free Optimization for Deep Neural Network
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy +1
Training deep neural network is a high dimensional and a highly non-convex optimization problem. Stochastic gradient descent (SGD) algorithm and it's variations are the current sta…
High Performance Emulation of Quantum Circuits
Thomas Häner, Damian S. Steiger, Mikhail Smelyanskiy +1
As quantum computers of non-trivial size become available in the near future, it is imperative to develop tools to emulate small quantum computers. This allows for validation and d…
Error Sensitivity to Environmental Noise in Quantum Circuits for Chemical State Preparation
Nicolas P. D. Sawaya, Mikhail Smelyanskiy, Jarrod R. McClean +1
Calculating molecular energies is likely to be one of the first useful applications to achieve quantum supremacy, performing faster on a quantum than a classical computer. However,…
qHiPSTER: The Quantum High Performance Software Testing Environment
Mikhail Smelyanskiy, Nicolas P. D. Sawaya, Alán Aspuru-Guzik
We present qHiPSTER, the Quantum High Performance Software Testing Environment. qHiPSTER is a distributed high-performance implementation of a quantum simulator on a classical comp…