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Review

2018

Review

2018

The magnetic fields generated by spins and currents provide a unique window into the physics of correlated-electron materials and… Expand

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Review

2018

Review

2018

Modern deep neural network training is typically based on mini-batch stochastic gradient optimization. While the use of large… Expand

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Review

2018

Review

2018

In decentralized optimization, nodes cooperate to minimize an overall objective function that is the sum (or average) of per-node… Expand

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Review

2018

Review

2018

Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline… Expand

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Review

2017

Review

2017

The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential… Expand

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Review

2017

Review

2017

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD… Expand

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Review

2016

Review

2016

This is an expository paper on the theory of gradient flows, and in particular of those PDEs which can be interpreted as gradient… Expand

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Highly Cited

2014

Highly Cited

2014

In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The… Expand

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Highly Cited

1999

Highly Cited

1999

Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and… Expand

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Highly Cited

1998

Highly Cited

1998

When a parameter space has a certain underlying structure, the ordinary gradient of a function does not represent its steepest… Expand

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