Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Szekely, Pedro A"'
Autor:
Benjamin, Daniel M., Morstatter, Fred, Abbas, Ali E., Abeliuk, Andres, Atanasov, Pavel, Bennett, Stephen, Beger, Andreas, Birari, Saurabh, Budescu, David V., Catasta, Michele, Ferrara, Emilio, Haravitch, Lucas, Himmelstein, Mark, Hossain, KSM Tozammel, Huang, Yuzhong, Jin, Woojeong, Joseph, Regina, Leskovec, Jure, Matsui, Akira, Mirtaheri, Mehrnoosh, Ren, Xiang, Satyukov, Gleb, Sethi, Rajiv, Singh, Amandeep, Sosic, Rok, Steyvers, Mark, Szekely, Pedro A, Ward, Michael D., Galstyan, Aram
Publikováno v:
AI Magazine, Volume 44, Issue 1, Pages 112-128, Spring 2023
Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machin
Externí odkaz:
http://arxiv.org/abs/2412.10981
Autor:
Cho, Hyundong, Jedema, Nicolaas, Ribeiro, Leonardo F. R., Sharma, Karishma, Szekely, Pedro, Moschitti, Alessandro, Janssen, Ruben, May, Jonathan
Current instruction-tuned language models are exclusively trained with textual preference data and thus are often not aligned with the unique requirements of other modalities, such as speech. To better align language models with the speech domain, we
Externí odkaz:
http://arxiv.org/abs/2409.14672
Entity resolution is the task of disambiguating records that refer to the same entity in the real world. In this work, we explore adapting one of the most efficient and accurate Jaccard-based entity resolution algorithms - PPJoin, to the private doma
Externí odkaz:
http://arxiv.org/abs/2208.07999
Large public knowledge graphs, like Wikidata, contain billions of statements about tens of millions of entities, thus inspiring various use cases to exploit such knowledge graphs. However, practice shows that much of the relevant information that fit
Externí odkaz:
http://arxiv.org/abs/2207.00143
Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structur
Externí odkaz:
http://arxiv.org/abs/2205.03972
Embedding methods have demonstrated robust performance on the task of link prediction in knowledge graphs, by mostly encoding entity relationships. Recent methods propose to enhance the loss function with a literal-aware term. In this paper, we propo
Externí odkaz:
http://arxiv.org/abs/2203.13965
Language models (LMs) show state of the art performance for common sense (CS) question answering, but whether this ability implies a human-level mastery of CS remains an open question. Understanding the limitations and strengths of LMs can help resea
Externí odkaz:
http://arxiv.org/abs/2201.07902
Tables provide valuable knowledge that can be used to verify textual statements. While a number of works have considered table-based fact verification, direct alignments of tabular data with tokens in textual statements are rarely available. Moreover
Externí odkaz:
http://arxiv.org/abs/2109.04053
Entity resolution is the task of identifying records in different datasets that refer to the same entity in the real world. In sensitive domains (e.g. financial accounts, hospital health records), entity resolution must meet privacy requirements to a
Externí odkaz:
http://arxiv.org/abs/2108.09879
While the similarity between two concept words has been evaluated and studied for decades, much less attention has been devoted to algorithms that can compute the similarity of nodes in very large knowledge graphs, like Wikidata. To facilitate invest
Externí odkaz:
http://arxiv.org/abs/2108.05410