4 citations · 9 across the 4 of their papers we have counts for
5 papers
On Optimizing Distributed Tucker Decomposition for Sparse Tensors
Venkatesan T. Chakaravarthy, Jee W. Choi, Douglas J. Joseph +4
The Tucker decomposition generalizes the notion of Singular Value Decomposition (SVD) to tensors, the higher dimensional analogues of matrices. We study the problem of constructing…
Efficient Training of Convolutional Neural Nets on Large Distributed Systems
Sameer Kumar, Dheeraj Sreedhar, Vaibhav Saxena +2
Deep Neural Networks (DNNs) have achieved im- pressive accuracy in many application domains including im- age classification. Training of DNNs is an extremely compute- intensive pr…
PowerAI DDL
Minsik Cho, Ulrich Finkler, Sameer Kumar +3
As deep neural networks become more complex and input datasets grow larger, it can take days or even weeks to train a deep neural network to the desired accuracy. Therefore, distri…
On Optimizing Distributed Tucker Decomposition for Dense Tensors
Venkatesan T Chakaravarthy, Jee W Choi, Douglas J Joseph +4
The Tucker decomposition expresses a given tensor as the product of a small core tensor and a set of factor matrices. Apart from providing data compression, the construction is use…
Single-Symbol ML Decodable Distributed STBCs for Partially-Coherent Cooperative Networks
D. Sreedhar, A. Chockalingam, B. Sundar Rajan
Space-time block codes (STBCs) that are single-symbol decodable (SSD) in a co-located multiple antenna setting need not be SSD in a distributed cooperative communication setting. A…