Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Szlàvik, Zoltán"'
Contrastive learning is a powerful way of learning multimodal representations across various domains such as image-caption retrieval and audio-visual representation learning. In this work, we investigate if these findings generalize to the domain of
Externí odkaz:
http://arxiv.org/abs/2309.00347
Autor:
Santamaria, Selene Baez, Manousogiannis, Emmanouil, Boomgaard, Guusje, Tran, Linh P., Szlavik, Zoltan, Sips, Robert-Jan
Background: Access to medical care is strongly dependent on resource allocation, such as the geographical distribution of medical facilities. Nevertheless, this data is usually restricted to country official documentation, not available to the public
Externí odkaz:
http://arxiv.org/abs/2204.05206
Autor:
Contempré, Edeline, Szlávik, Zoltán, Mohammadi, Majid, Velazquez, Erick, Teije, Annette ten, Tiddi, Ilaria
Health care professionals rely on treatment search engines to efficiently find adequate clinical trials and early access programs for their patients. However, doctors lose trust in the system if its underlying processes are unclear and unexplained. I
Externí odkaz:
http://arxiv.org/abs/2110.12891
Autor:
Draws, Tim, Szlávik, Zoltán, Timmermans, Benjamin, Tintarev, Nava, Varshney, Kush R., Hind, Michael
Publikováno v:
Persuasive Technology, Cham, 2021, p. 135-149
Systems aiming to aid consumers in their decision-making (e.g., by implementing persuasive techniques) are more likely to be effective when consumers trust them. However, recent research has demonstrated that the machine learning algorithms that ofte
Externí odkaz:
http://arxiv.org/abs/2101.12715
In this paper, we explore how to efficiently combine crowdsourcing and machine intelligence for the problem of document screening, where we need to screen documents with a set of machine-learning filters. Specifically, we focus on building a set of m
Externí odkaz:
http://arxiv.org/abs/2012.02297
Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed. Here we put forward one limitation which arises from a lack of adaptation of ML
Externí odkaz:
http://arxiv.org/abs/1911.02455
Publikováno v:
In Information Processing and Management September 2012 48(5):956-968
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Akademický článek
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