Zobrazeno 1 - 10
of 2 987
pro vyhledávání: '"Hoffart A"'
Autor:
Spinaci, Marco, Polewczyk, Marek, Hoffart, Johannes, Kohler, Markus C., Thelin, Sam, Klein, Tassilo
Self-supervised learning on tabular data seeks to apply advances from natural language and image domains to the diverse domain of tables. However, current techniques often struggle with integrating multi-domain data and require data cleaning or speci
Externí odkaz:
http://arxiv.org/abs/2410.13516
Autor:
Kampik, Timotheus, Warmuth, Christian, Rebmann, Adrian, Agam, Ron, Egger, Lukas N. P., Gerber, Andreas, Hoffart, Johannes, Kolk, Jonas, Herzig, Philipp, Decker, Gero, van der Aa, Han, Polyvyanyy, Artem, Rinderle-Ma, Stefanie, Weber, Ingo, Weidlich, Matthias
The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over rigidly defined symbolic models, but also serves as a proof-point of
Externí odkaz:
http://arxiv.org/abs/2309.00900
Autor:
Mary M. Barker, Kadri Kõiv, Ingibjörg Magnúsdóttir, Hannah Milbourn, Bin Wang, Xinkai Du, Gillian Murphy, Eva Herweijer, Elísabet U. Gísladóttir, Huiqi Li, Anikó Lovik, Anna K. Kähler, Archie Campbell, Maria Feychting, Arna Hauksdóttir, Emily E. Joyce, Edda Bjork Thordardottir, Emma M. Frans, Asle Hoffart, Reedik Mägi, Gunnar Tómasson, Kristjana Ásbjörnsdóttir, Jóhanna Jakobsdóttir, Ole A. Andreassen, Patrick F. Sullivan, Sverre Urnes Johnson, Thor Aspelund, Ragnhild Eek Brandlistuen, Helga Ask, Daniel L. McCartney, Omid V. Ebrahimi, Kelli Lehto, Unnur A. Valdimarsdóttir, Fredrik Nyberg, Fang Fang
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Individuals with mental illness are at higher risk of severe COVID-19 outcomes. However, previous studies on the uptake of COVID-19 vaccination in this population have reported conflicting results. Using data from seven cohort studies (N = 3
Externí odkaz:
https://doaj.org/article/30508fc0e71b4bbeba07007ae76b359c
Autor:
Kristin Stegenga, William R. Black, Jennifer Christofferson, Dustin P. Wallace, Cara M. Hoffart
Publikováno v:
Discover Psychology, Vol 4, Iss 1, Pp 1-11 (2024)
Abstract Objectives Chronic pain in children and adolescents is often associated with functional, physical, and psychosocial challenges. Intensive interdisciplinary pain treatment (IIPT) programs are effective at helping these youth regain functionin
Externí odkaz:
https://doaj.org/article/53de85c727b54c49a0155dfbaa190b2f
Autor:
Bastos, Anson, Singh, Kuldeep, Nadgeri, Abhishek, Hoffart, Johannes, Suzumura, Toyotaro, Singh, Manish
In this paper we present a novel method, $\textit{Knowledge Persistence}$ ($\mathcal{KP}$), for faster evaluation of Knowledge Graph (KG) completion approaches. Current ranking-based evaluation is quadratic in the size of the KG, leading to long eval
Externí odkaz:
http://arxiv.org/abs/2301.12929
Publikováno v:
Frontiers in Environmental Economics, Vol 3 (2024)
Externí odkaz:
https://doaj.org/article/8d16d4e6ec804c5688d62cad6a6a182f
Autor:
Franziska M. Hoffart, Franziska Holz
Publikováno v:
Frontiers in Environmental Economics, Vol 3 (2024)
Climate policy will inevitably lead to the stranding of fossil energy assets such as production and transport assets for coal, oil, and natural gas. Resource-rich developing countries are particularly affected, as they have a higher risk of asset str
Externí odkaz:
https://doaj.org/article/5c6d728cbb874bf899950a4ba5701d82
Autor:
Bastos, Anson, Singh, Kuldeep, Nadgeri, Abhishek, Shekarpour, Saeedeh, Mulang, Isaiah Onando, Hoffart, Johannes
Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to represent entities and relations in dense vector space and employed in downstream tasks such as link prediction. A few KGE techniques address interpretability, i.e., ma
Externí odkaz:
http://arxiv.org/abs/2108.05774
Publikováno v:
In Applied Energy 1 May 2024 361
Autor:
Nadgeri, Abhishek, Bastos, Anson, Singh, Kuldeep, Mulang', Isaiah Onando, Hoffart, Johannes, Shekarpour, Saeedeh, Saraswat, Vijay
We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG). Especially in this presumed sentential RE setting, the context of a single sente
Externí odkaz:
http://arxiv.org/abs/2106.00459