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pro vyhledávání: '"Qian, Sharon"'
For many optimization problems in machine learning, finding an optimal solution is computationally intractable and we seek algorithms that perform well in practice. Since computational intractability often results from pathological instances, we look
Externí odkaz:
http://arxiv.org/abs/2102.11911
Autor:
Vig, Jesse, Gehrmann, Sebastian, Belinkov, Yonatan, Qian, Sharon, Nevo, Daniel, Sakenis, Simas, Huang, Jason, Singer, Yaron, Shieber, Stuart
Common methods for interpreting neural models in natural language processing typically examine either their structure or their behavior, but not both. We propose a methodology grounded in the theory of causal mediation analysis for interpreting which
Externí odkaz:
http://arxiv.org/abs/2004.12265
Despite the vast success of Deep Neural Networks in numerous application domains, it has been shown that such models are not robust i.e., they are vulnerable to small adversarial perturbations of the input. While extensive work has been done on why s
Externí odkaz:
http://arxiv.org/abs/2002.09422
Autor:
Qian, Sharon, Singer, Yaron
In this paper, we propose a new framework for designing fast parallel algorithms for fundamental statistical subset selection tasks that include feature selection and experimental design. Such tasks are known to be weakly submodular and are amenable
Externí odkaz:
http://arxiv.org/abs/1903.02656
Publikováno v:
The Journal of Economic Education, 2017 Jan 01. 48(3), 146-166.
Externí odkaz:
https://www.jstor.org/stable/48542327
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