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Gradient descent

Known as: Descent, Gradient descent optimization, Gradient descent method 
Gradient descent is a first-order iterative optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and… Expand
Review
2019
Review
2019
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate… Expand
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Review
2019
Review
2019
Deep learning achieves state-of-the-art results in many areas. However recent works have shown that deep networks can be… Expand
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Review
2019
Review
2019
Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix… Expand
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Review
2018
Review
2018
Abstract Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in… Expand
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Highly Cited
2016
Highly Cited
2016
The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this… Expand
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Highly Cited
2005
Highly Cited
2005
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function… Expand
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Highly Cited
2005
Highly Cited
2005
We study a general online convex optimization problem. We have a convex set <i>S</i> and an unknown sequence of cost functions <i… Expand
Highly Cited
1999
Highly Cited
1999
We provide an abstract characterization of boosting algorithms as gradient decsent on cost-functionals in an inner-product… Expand
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Highly Cited
1997
Highly Cited
1997
We consider two algorithm for on-line prediction based on a linear model. The algorithms are the well-known Gradient Descent (GD… Expand
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