Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Nanavati, Praharsh"'
Autor:
Nanavati, Praharsh, Prasad, Ranjitha
Explainable AI is an evolving area that deals with understanding the decision making of machine learning models so that these models are more transparent, accountable, and understandable for humans. In particular, post-hoc model-agnostic interpretabl
Externí odkaz:
http://arxiv.org/abs/2307.00680
Autor:
Constantinou, Anthony, Kitson, Neville K., Liu, Yang, Chobtham, Kiattikun, Hashemzadeh, Arian, Nanavati, Praharsh A., Mbuvha, Rendani, Petrungaro, Bruno
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algorithms enables transparency and explainability, which is necessary for
Externí odkaz:
http://arxiv.org/abs/2305.03859
Player synergies are a salient feature of team sports. In the team game of cricket, player synergies may be reflected in batting partnerships. Batting partnerships have been analysed extensively. In this paper, we introduce and precisely define bowli
Externí odkaz:
http://arxiv.org/abs/2108.12667
Autor:
Constantinou, Anthony C., Kitson, Neville K., Liu, Yang, Chobtham, Kiattikun, Hashemzadeh, Arian, Nanavati, Praharsh A., Mbuvha, Rendani, Petrungaro, Bruno
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation provided by these algorithms enables transparency and explainability, which is necessary in cr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db6ff442f69e863df76a66fd21dce242