Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Arya Vijay"'
Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level. Although many variants have been proposed, few provide a simple way to produce high fidelity e
Externí odkaz:
http://arxiv.org/abs/2201.12143
Autor:
Arya, Vijay, Bellamy, Rachel K. E., Chen, Pin-Yu, Dhurandhar, Amit, Hind, Michael, Hoffman, Samuel C., Houde, Stephanie, Liao, Q. Vera, Luss, Ronny, Mojsilovic, Aleksandra, Mourad, Sami, Pedemonte, Pablo, Raghavendra, Ramya, Richards, John, Sattigeri, Prasanna, Shanmugam, Karthikeyan, Singh, Moninder, Varshney, Kush R., Wei, Dennis, Zhang, Yunfeng
Publikováno v:
IAAI 2022
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations. At the same time, these stakeholders, whether they be affected citize
Externí odkaz:
http://arxiv.org/abs/2109.12151
To preserve anonymity and obfuscate their identity on online platforms users may morph their text and portray themselves as a different gender or demographic. Similarly, a chatbot may need to customize its communication style to improve engagement wi
Externí odkaz:
http://arxiv.org/abs/2001.06693
Autor:
Somy, Nishant Baranwal, Kannan, Kalapriya, Arya, Vijay, Hans, Sandeep, Singh, Abhishek, Lohia, Pranay, Mehta, Sameep
Publikováno v:
IEEE International Conference on Blockchain, Blockchain 2019
We present a blockchain based system that allows data owners, cloud vendors, and AI developers to collaboratively train machine learning models in a trustless AI marketplace. Data is a highly valued digital asset and central to deriving business insi
Externí odkaz:
http://arxiv.org/abs/2001.09011
Autor:
Arya, Vijay, Bellamy, Rachel K. E., Chen, Pin-Yu, Dhurandhar, Amit, Hind, Michael, Hoffman, Samuel C., Houde, Stephanie, Liao, Q. Vera, Luss, Ronny, Mojsilović, Aleksandra, Mourad, Sami, Pedemonte, Pablo, Raghavendra, Ramya, Richards, John, Sattigeri, Prasanna, Shanmugam, Karthikeyan, Singh, Moninder, Varshney, Kush R., Wei, Dennis, Zhang, Yunfeng
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affecte
Externí odkaz:
http://arxiv.org/abs/1909.03012
There has been an unprecedented surge in the number of service providers offering a wide range of machine learning prediction APIs for tasks such as image classification, language translation, etc. thereby monetizing the underlying data and trained m
Externí odkaz:
http://arxiv.org/abs/1812.02154
Cloud vendors are increasingly offering machine learning services as part of their platform and services portfolios. These services enable the deployment of machine learning models on the cloud that are offered on a pay-per-query basis to application
Externí odkaz:
http://arxiv.org/abs/1711.07221
Publikováno v:
Indian Economic Journal; Oct2024, Vol. 72 Issue 5, p807-815, 9p
We consider a system of m linear equations in n variables Ax=b where A is a given m x n matrix and b is a given m-vector known to be equal to Ax' for some unknown solution x' that is integer and k-sparse: x' in {0,1}^n and exactly k entries of x' are
Externí odkaz:
http://arxiv.org/abs/1112.1757
Autor:
Arya, Vijay, Veitch, Darryl
We study network loss tomography based on observing average loss rates over a set of paths forming a tree -- a severely underdetermined linear problem for the unknown link loss probabilities. We examine in detail the role of sparsity as a regularisin
Externí odkaz:
http://arxiv.org/abs/1108.1377