1.4k citations · 3.2k across the 73 of their papers we have counts for
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cs.LG2019★ 6 cited
Solving general elliptical mixture models through an approximate Wasserstein manifold
Shengxi Li, Zeyang Yu, Min Xiang +1
We address the estimation problem for general finite mixture models, with a particular focus on the elliptical mixture models (EMMs). Compared to the widely adopted Kullback-Leible…
cs.LG2019★ 21 cited
Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation
Giuseppe G. Calvi, Ahmad Moniri, Mahmoud Mahfouz +2
This work aims to help resolve the two main stumbling blocks in the application of Deep Neural Networks (DNNs), that is, the exceedingly large number of trainable parameters and th…