Regulating Orthogonality Of Feature Functions For Highly Compressed Deep Neural Networks
When designing deep neural networks (DNN), the number of nodes in hidden layers can have a profound impact on the performance of the model. The information carried by the nodes in each layer creates a subspace, whose dimensionality is determined by the number of nodes and their linear dependency. Th...
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/144614 https://orcid.org/0000-0003-0824-5945 |