Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Mark T. Keane"'
Publikováno v:
Data in Brief, Vol 44, Iss , Pp 108545- (2022)
With this article, we present a repository containing datasets, analysis code, and some outputs related to a paper in press at Cognition. The data were collected as part of a pre-test, pilot test, and main study all designed in SurveyGizmo and partic
Externí odkaz:
https://doaj.org/article/ed5aa76ce36c42bb9a73ac19b971d156
Autor:
Mohammed Temraz, Mark T. Keane
Publikováno v:
Machine Learning with Applications, Vol 9, Iss , Pp 100375- (2022)
Learning from class imbalanced datasets poses challenges for many machine learning algorithms. Many real-world domains are, by definition, class imbalanced by virtue of having a majority class that naturally has many more instances than its minority
Externí odkaz:
https://doaj.org/article/6f1824bc7bf148fea51f8543175e2626
Autor:
Molly S. Quinn, Mark T. Keane
Publikováno v:
Data in Brief, Vol 35, Iss , Pp 106935- (2021)
The three datasets described in this paper were collected from online experiments distributed via Prolific.co participant system. Together, the three datasets comprise 9720 text responses of unexpected events participants predicted for everyday scena
Externí odkaz:
https://doaj.org/article/4f9969be1ac14f518896dd8682d30f96
Publikováno v:
Frontiers in Psychology, Vol 9 (2018)
In this article we explore the relationship between learning and the conjunction fallacy. The interpretation of the conjunction effect as a fallacy assumes that all observers share the same knowledge, and that nobody has access to privileged informat
Externí odkaz:
https://doaj.org/article/0aa57e8b67104c8eac248281d3fd698c
Publikováno v:
Proceedings of the 28th International Conference on Intelligent User Interfaces.
Publikováno v:
Explainable Deep Learning AI ISBN: 9780323960984
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb5d3161921a898f475ba504faadbc42
https://doi.org/10.1016/b978-0-32-396098-4.00019-3
https://doi.org/10.1016/b978-0-32-396098-4.00019-3
Autor:
Kamila Abdiyeva, Narendra Ahuja, Mathias Anneken, David Auber, Meghna P. Ayyar, Romaissa Beddiar, Jenny Benois-Pineau, Jesús Bescós, Ilaria Boscolo Galazzo, Romain Bourqui, Lorenza Brusini, Nadia Burkart, Massimiliano Calabrese, Federica Cruciani, Eoin Delaney, Rachid Deriche, Marcos Escudero-Viñolo, Andrija Gajić, Damien Garreau, Giorgio Giacinto, Romain Giot, Oleksii Gorokhovatskyi, Volodymyr Gorokhovatskyi, Derek Greene, Adrien Halnaut, Alexandre Hardouin, Marco F. Huber, Gaëlle Jouis, Mark T. Keane, Eoin M. Kenny, Alejandro López-Cifuentes, Martin Lukac, Gloria Menegaz, Harold Mouchère, Mourad Oussalah, Olena Peredrii, Dragutin Petkovic, Fabien Picarougne, Georges Quénot, Gustavo Retuci Pinheiro, Konrad Rieck, Leticia Rittner, Wojciech Samek, Michele Scalas, Francesco Setti, Manjunatha Veerappa, Nataliia Vlasenko, Akka Zemmari, Mauro Zucchelli
Publikováno v:
Explainable Deep Learning AI ISBN: 9780323960984
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56e692de26d1b3dc8488a52ea6e2244c
https://doi.org/10.1016/b978-0-32-396098-4.00005-3
https://doi.org/10.1016/b978-0-32-396098-4.00005-3
Publikováno v:
Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society.
Publikováno v:
Éire-Ireland. 55:247-251
Publikováno v:
Case-Based Reasoning Research and Development ISBN: 9783031149221
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20eab09e8ddbfb93153e342feeab2573
https://doi.org/10.1007/978-3-031-14923-8_24
https://doi.org/10.1007/978-3-031-14923-8_24