deep linear network
Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network
Unfortunately, even for standard linear networks in regression setting, a comprehensive characterization of the implicit bias is still an open problem.
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Step Size Matters in Deep Learning
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Exact natural gradient in deep linear networks and its application to the nonlinear case
Alberto Bernacchia, Mate Lengyel, Guillaume Hennequin
Neural Information Processing Systems http://nips.cc/
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Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
However, there is currently no framework to apply it to generic architectures, specifically ones with linear weight-sharing layers.
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Global Convergence of Gradient Descent for Deep Linear Residual Networks
It is motivated by avoiding stable manifolds of saddle points.
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24ec8468b67314c2013d215b77034476-Supplemental.pdf
Deep neural networks have been successfully trained with simple gradient-based methods, despite the inherent non-convexity of the objectivefunction.
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