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Gradient

Known as: Grad, Gradient Operator, Gradient of a scalar 
In mathematics, the gradient is a generalization of the usual concept of derivative to functions of several variables. If f(x1, ..., xn) is a… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
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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