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of 37
pro vyhledávání: '"Radhakrishnan, Venkatesh"'
Autor:
Ramasubramanian, Shrinivas, Rangwani, Harsh, Takemori, Sho, Samanta, Kunal, Umeda, Yuhei, Radhakrishnan, Venkatesh Babu
The rise in internet usage has led to the generation of massive amounts of data, resulting in the adoption of various supervised and semi-supervised machine learning algorithms, which can effectively utilize the colossal amount of data to train model
Externí odkaz:
http://arxiv.org/abs/2403.18301
Autor:
Rangwani, Harsh, Ramasubramanian, Shrinivas, Takemori, Sho, Takashi, Kato, Umeda, Yuhei, Radhakrishnan, Venkatesh Babu
Self-training based semi-supervised learning algorithms have enabled the learning of highly accurate deep neural networks, using only a fraction of labeled data. However, the majority of work on self-training has focused on the objective of improving
Externí odkaz:
http://arxiv.org/abs/2304.14738
Autor:
Niu, Xing, Kapoor, Raghav, Glavic, Boris, Gawlick, Dieter, Liu, Zhen Hua, Krishnaswamy, Vasudha, Radhakrishnan, Venkatesh
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018
A well-established technique for capturing database provenance as annotations on data is to instrument queries to propagate such annotations. However, even sophisticated query optimizers often fail to produce efficient execution plans for instrumente
Externí odkaz:
http://arxiv.org/abs/1804.07156
The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. Similarly, performance on multi-disciplinary tasks such as Visual Question Answering (VQA) is considered
Externí odkaz:
http://arxiv.org/abs/1801.09356
A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typi
Externí odkaz:
http://arxiv.org/abs/1706.02071
Autor:
Arab, Bahareh Sadat, Gawlick, Dieter, Krishnaswamy, Vasudha, Radhakrishnan, Venkatesh, Glavic, Boris
Provenance for transactional updates is critical for many applications such as auditing and debugging of transactions. Recently, we have introduced MV-semirings, an extension of the semiring provenance model that supports updates and transactions. Fu
Externí odkaz:
http://arxiv.org/abs/1608.08258
Publikováno v:
SIAM J. Computing, Vol. 27, No 5, Oct. 1998, pp. 1237--1261
We study the efficient approximability of basic graph and logic problems in the literature when instances are specified hierarchically as in \cite{Le89} or are specified by 1-dimensional finite narrow periodic specifications as in \cite{Wa93}. We sho
Externí odkaz:
http://arxiv.org/abs/cs/9809064
We prove the #P-hardness of the counting problems associated with various satisfiability, graph and combinatorial problems, when restricted to planar instances. These problems include \begin{romannum} \item[{}] {\sc 3Sat, 1-3Sat, 1-Ex3Sat, Minimum Ve
Externí odkaz:
http://arxiv.org/abs/cs/9809017
Akademický článek
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Autor:
Hunt, Harry B, III, Marathe, Madhav V, Radhakrishnan, Venkatesh, Ravi, S.S, Rosenkrantz, Daniel J, Stearns, Richard E
Publikováno v:
In Information and Computation 25 February 2002 173(1):40-63
