2015
NIPS
NeurIPS 2015
Path-SGD: Path-Normalized Optimization in Deep Neural Networks
Abstract
We revisit the choice of SGD for training deep neural networks by reconsidering the appropriate geometry in which to optimize the weights. We argue for a geometry invariant to rescaling of weights that does not affect the output of the network, and suggest Path-SGD, which is an approximate steepest descent method with respect to a path-wise regularizer related to max-norm regularization. Path-SGD is easy and efficient to implement and leads to empirical gains over SGD and AdaGrad.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Trend Setter
— Neural Network Optimization
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Keyword Pioneer
— path normalization
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Hot Topic Early Bird
— neural network optimization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio