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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

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

Review

2019

Review

2019

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix… Expand

Review

2018

Review

2018

Abstract Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in… Expand

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

Highly Cited

2005

Highly Cited

2005

We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function… Expand

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

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