Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Chakaravarthy, Venkatesan"'
Autor:
Ranjan, Rishabh, Grover, Siddharth, Medya, Sourav, Chakaravarthy, Venkatesan, Sabharwal, Yogish, Ranu, Sayan
Among various distance functions for graphs, graph and subgraph edit distances (GED and SED respectively) are two of the most popular and expressive measures. Unfortunately, exact computations for both are NP-hard. To overcome this computational bott
Externí odkaz:
http://arxiv.org/abs/2112.13143
Autor:
Chakaravarthy, Venkatesan T., Seshadri, Padmanabha V., Aggarwal, Pooja, Choudhury, Anamitra R., Kumar, Ashok Pon, Sabharwal, Yogish, Singhee, Amith
In conventional public clouds, designing a suitable initial cluster for a given application workload is important in reducing the computational foot-print during run-time. In edge or on-premise clouds, cold-start rightsizing the cluster at the time o
Externí odkaz:
http://arxiv.org/abs/2112.11597
Autor:
Chakaravarthy, Venkatesan T., Pandian, Shivmaran S., Raje, Saurabh, Sabharwal, Yogish, Suzumura, Toyotaro, Ubaru, Shashanka
We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems. To the best of our knowledge, this is the first scaling study on dynamic GNN. We devise mechanisms for re
Externí odkaz:
http://arxiv.org/abs/2109.07893
Autor:
Goyal, Saurabh, Choudhury, Anamitra R., Raje, Saurabh M., Chakaravarthy, Venkatesan T., Sabharwal, Yogish, Verma, Ashish
We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pertaining to word-vectors (intermediate encoder outputs) and eliminating
Externí odkaz:
http://arxiv.org/abs/2001.08950
Autor:
Chakaravarthy, Venkatesan T., Choi, Jee W., Joseph, Douglas J., Murali, Prakash, Pandian, Shivmaran S., Sabharwal, Yogish, Sreedhar, Dheeraj
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 the Tucker decomposition of sparse tensors on distributed memory syst
Externí odkaz:
http://arxiv.org/abs/1804.09494
Autor:
Chakaravarthy, Venkatesan T, Choi, Jee W, Joseph, Douglas J, Liu, Xing, Murali, Prakash, Sabharwal, Yogish, Sreedhar, Dheeraj
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 useful in performing analysis such as principal component analysis (PCA)a
Externí odkaz:
http://arxiv.org/abs/1707.05594
Autor:
Aggarwal, Anshul, Chakaravarthy, Venkatesan T., Gupta, Neelima, Sabharwal, Yogish, Sharma, Sachin, Thakral, Sonika
We consider the replica placement problem: given a graph with clients and nodes, place replicas on a minimum set of nodes to serve all the clients; each client is associated with a request and maximum distance that it can travel to get served and the
Externí odkaz:
http://arxiv.org/abs/1705.00145
Autor:
Chakaravarthy, Venkatesan T., Kapralov, Michael, Murali, Prakash, Petrini, Fabrizio, Que, Xinyu, Sabharwal, Yogish, Schieber, Baruch
The problem of counting occurrences of query graphs in a large data graph, known as subgraph counting, is fundamental to several domains such as genomics and social network analysis. Many important special cases (e.g. triangle counting) have received
Externí odkaz:
http://arxiv.org/abs/1602.04478
Publikováno v:
In Discrete Optimization November 2020 38
Autor:
Chakaravarthy, Venkatesan T.
Thesis (Ph. D.)--University of Wisconsin--Madison, 2004.
Includes bibliographical references (p. 115-121). Also available on the Internet.
Includes bibliographical references (p. 115-121). Also available on the Internet.