New insights into training dynamics of deep classifiers : vi

MIT Center for Brains, Minds and Machines researchers provide one of the first theoretical analyses covering optimization, generalization, and approximation in deep networks and offers new insights into the properties that emerge during training.

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Tomaso Poggio , Akshay Rangamani , Department Of Brain , Mcgovern Institute , Cognitive Sciences , Brown University , Deep Classifiers , Square Loss , Slow Rank , Neural Collapse , Generalization Bounds , Govern Institute , Eugene Mcdermott Professor , Mit Center For Brains Minds And Machines , Omer Galanti , Onvolutional Neural Networks Cnns , Eep Neural Networks Dnns , Ully Connected Deep Networks , Stochastic Gradient Descent , Weight Decay Regularization , Eight Normalization , Ow Rank Bias , Ocalized Kernels , Deep Learning ,