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Some of the next articles are maybe not open access.
2017
This chapter gives a broad overview and a historical context around the subject of deep learning. It also gives the reader a roadmap for navigating the book, the prerequisites, and further reading to dive deeper into the subject matter.
openaire +1 more source
This chapter gives a broad overview and a historical context around the subject of deep learning. It also gives the reader a roadmap for navigating the book, the prerequisites, and further reading to dive deeper into the subject matter.
openaire +1 more source
Online Covariance Matrix Estimation in Stochastic Gradient Descent
Journal of the American Statistical Association, 2023exaly
Robust Stochastic Gradient Descent With Student-t Distribution Based First-Order Momentum
IEEE Transactions on Neural Networks and Learning Systems, 2022Wendyam Eric Lionel Ilboudo +2 more
exaly
Resampling Stochastic Gradient Descent Cheaply
2023 Winter Simulation Conference (WSC), 2023Henry Lam, Zitong Wang 0005
openaire +2 more sources
Communication-Censored Distributed Stochastic Gradient Descent
IEEE Transactions on Neural Networks and Learning Systems, 2022Liping Li, Weiyu Li, Tianyi Chen
exaly
Stochastic Compositional Gradient Descent Under Compositional Constraints
IEEE Transactions on Signal Processing, 2023Srujan Thomdapu, Ketan Rajawat
exaly
On the Convergence of Stochastic Gradient Descent for Nonlinear Ill-Posed Problems
SIAM Journal on Optimization, 2020Bangti Jin
exaly