Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Abstract A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Y Dauphin, R Pascanu, C Gulcehre… - arXiv preprint arXiv: …, 2014 - adsabs.harvard.edu
Abstract A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
YN Dauphin, R Pascanu, C Gulcehre, K Cho… - Advances in Neural …, 2014 - papers.nips.cc
Abstract A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
[PDF][PDF] Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
YN Dauphin, R Pascanu… - Advances in …, 2014 - machinelearning.wustl.edu
Abstract A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
[PDF][PDF] Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
YNDRPCG KyunghyunCho, S Ganguli, Y Bengio - pdfs.semanticscholar.org
Abstract A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Y Dauphin, R Pascanu, C Gulcehre, K Cho… - arXiv preprint arXiv: …, 2014 - arxiv.org
Abstract: A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
[PDF][PDF] Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
YNDRPCG KyunghyunCho, S Ganguli, Y Bengio - ganguli-gang.stanford.edu
Abstract A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
[PDF][PDF] Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
YN Dauphin, R Pascanu, C Gulcehre, K Cho… - researchgate.net
Abstract A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
[PDF][PDF] Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
YN Dauphin, R Pascanu, C Gulcehre, K Cho… - datascienceassn.org
Abstract A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
[PDF][PDF] Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
YNDRPCG KyunghyunCho, S Ganguli, Y Bengio - ganguli-gang.stanford.edu
Abstract A central challenge to many fields of science and engineering involves minimizing
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
non-convex error functions over continuous, high dimensional spaces. Gradient descent or
quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it ...
