Zobrazeno 1 - 10
of 33
pro vyhledávání: '"DAEE, PEDRAM"'
A central concern in an interactive intelligent system is optimization of its actions, to be maximally helpful to its human user. In recommender systems for instance, the action is to choose what to recommend, and the optimization task is to recommen
Externí odkaz:
http://arxiv.org/abs/2005.01291
Understanding each other is the key to success in collaboration. For humans, attributing mental states to others, the theory of mind, provides the crucial advantage. We argue for formulating human--AI interaction as a multi-agent problem, endowing AI
Externí odkaz:
http://arxiv.org/abs/1912.05284
Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teacher provides data that are consistent with the true data distribution.
Externí odkaz:
http://arxiv.org/abs/1809.02869
In human-in-the-loop machine learning, the user provides information beyond that in the training data. Many algorithms and user interfaces have been designed to optimize and facilitate this human--machine interaction; however, fewer studies have addr
Externí odkaz:
http://arxiv.org/abs/1710.04881
Autor:
Sundin, Iiris, Peltola, Tomi, Majumder, Muntasir Mamun, Daee, Pedram, Soare, Marta, Afrabandpey, Homayun, Heckman, Caroline, Kaski, Samuel, Marttinen, Pekka
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which to base the predictions remains a challenge, especially when the sampl
Externí odkaz:
http://arxiv.org/abs/1705.03290
Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging. This setting is encountered in multiple applications, such as precision medicine, where obtaining additional samples can be extremel
Externí odkaz:
http://arxiv.org/abs/1612.03328
Autor:
Vuong, Tung, Andolina, Salvatore, Jacucci, Giulio, Daee, Pedram, Klouche, Khalil, Sjöberg, Mats, Ruotsalo, Tuukka, Kaski, Samuel
openaire: EC/H2020/826266/EU//CO-ADAPT Everyday digital tasks can highly benefit from systems that recommend the right information to use at the right time. However, existing solutions typically support only specific applications and tasks. In this d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______661::01c9de2bac0a5ff37267134fc7041b3d
https://hdl.handle.net/10138/334390
https://hdl.handle.net/10138/334390
Akademický článek
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Understanding each other is the key to success in collaboration. For humans, attributing mental states to others, the theory of mind, provides the crucial advantage. We argue for formulating human--AI interaction as a multi-agent problem, endowing AI
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9499f33a47124004d11f0f61aed6d029
http://arxiv.org/abs/1912.05284
http://arxiv.org/abs/1912.05284
Autor:
Jacucci, Giulio, Barral, Oswald, Daee, Pedram, Wenjel, Markus, Serim, Baris, Ruotsalo, Tuukka, Pluchino, Patrik, Freeman, Jonathan, Gamberini, Luciano, Kaski, Samuel, Blankertz, Benjamin
The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiological responses and retrieving documents are characterized by uncertainty due to noisy signals and incomp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::1f577956788219d438918a6f20ff4a6c