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of 125
pro vyhledávání: '"Szlávik, 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
Autor:
I-Jen Chen, Proszenyák Ágnes, Zoltán B. Szabó, Levente Ondi, Csékei Márton, Olivier Geneste, Szabolcs Sipos, Allan E. Surgenor, Frédéric Colland, C. Pedder, Maïa Chanrion, Balázs Bálint, Ana-Leticia Maragno, Roderick E. Hubbard, Szlávik Zoltán, Pawel Dokurno, András Kotschy, James B. Murray, James R. Davidson
Publikováno v:
ACS Omega, Vol 6, Iss 34, Pp 22073-22102 (2021)
ACS Omega
ACS Omega
Following the identification of thieno[2,3-d]pyrimidine-based selective and potent inhibitors of MCL-1, we explored the effect of core swapping at different levels of advancement. During hit-to-lead optimization, X-ray-guided S-N replacement in the c
Akademický článek
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Publikováno v:
In Information Processing and Management September 2012 48(5):956-968
Autor:
Boomgaard, Guusje, Santamaría, Selene Báez, Tiddi, Ilaria, Sips, Robert Jan, Szlávik, Zoltán, Martin, Andreas, Hinkelmann, Knut, Fill, Hans-Georg, Gerber, Aurona, Lenat, Doug, Stolle, Reinhard, van Harmelen, Frank
Publikováno v:
AAAI-MAKE 2021 Combining Machine Learning and Knowledge Engineering: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) Stanford University, Palo Alto, California, USA, March 22-24, 2021, 1-13
STARTPAGE=1;ENDPAGE=13;TITLE=AAAI-MAKE 2021 Combining Machine Learning and Knowledge Engineering
STARTPAGE=1;ENDPAGE=13;TITLE=AAAI-MAKE 2021 Combining Machine Learning and Knowledge Engineering
Query popularity is a main feature in web-search auto-completion. Several personalization features have been proposed to support specific users' searches, but often do not meet the privacy requirements of a medical environment (e.g. clinical trial se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::1e241b62b01ae9f58c8b61edafa8b081
https://research.vu.nl/en/publications/44fdca3c-d96e-4591-8297-8f6588800b83
https://research.vu.nl/en/publications/44fdca3c-d96e-4591-8297-8f6588800b83