Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Mulang, Isaiah Onando"'
Autor:
Bastos, Anson, Nadgeri, Abhishek, Singh, Kuldeep, Kanezashi, Hiroki, Suzumura, Toyotaro, Mulang', Isaiah Onando
The recent works proposing transformer-based models for graphs have proven the inadequacy of Vanilla Transformer for graph representation learning. To understand this inadequacy, there is a need to investigate if spectral analysis of the transformer
Externí odkaz:
http://arxiv.org/abs/2201.09332
Autor:
Tadesse, Girmaw Abebe, Ogallo, William, Wanjiru, Catherine, Wachira, Charles, Mulang', Isaiah Onando, Anand, Vibha, Walcott-Bryant, Aisha, Speakman, Skyler
Anomalous pattern detection aims to identify instances where deviation from normalcy is evident, and is widely applicable across domains. Multiple anomalous detection techniques have been proposed in the state of the art. However, there is a common l
Externí odkaz:
http://arxiv.org/abs/2201.02008
Analyzing the behaviour of a population in response to disease and interventions is critical to unearth variability in healthcare as well as understand sub-populations that require specialized attention, but also to assist in designing future interve
Externí odkaz:
http://arxiv.org/abs/2111.14622
Autor:
Wanjiru, Catherine, Ogallo, William, Tadesse, Girmaw Abebe, Wachira, Charles, Mulang', Isaiah Onando, Walcott-Bryant, Aisha
An automated feature selection pipeline was developed using several state-of-the-art feature selection techniques to select optimal features for Differentiating Patterns of Care (DPOC). The pipeline included three types of feature selection technique
Externí odkaz:
http://arxiv.org/abs/2111.03495
In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice. Students are good at understanding natural language questions and based on their domain knowledge can easily
Externí odkaz:
http://arxiv.org/abs/2110.09036
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
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
Autor:
Ravi, Manoj Prabhakar Kannan, Singh, Kuldeep, Mulang', Isaiah Onando, Shekarpour, Saeedeh, Hoffart, Johannes, Lehmann, Jens
In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to accomplish the EL task. The first transformer
Externí odkaz:
http://arxiv.org/abs/2101.09969
Autor:
Bastos, Anson, Nadgeri, Abhishek, Singh, Kuldeep, Mulang', Isaiah Onando, Shekarpour, Saeedeh, Hoffart, Johannes, Kaul, Manohar
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). RECON uses a graph neural network to learn representations of both the
Externí odkaz:
http://arxiv.org/abs/2009.08694
Autor:
Mulang', Isaiah Onando, Singh, Kuldeep, Prabhu, Chaitali, Nadgeri, Abhishek, Hoffart, Johannes, Lehmann, Jens
Publikováno v:
CIKM 2020
Pretrained Transformer models have emerged as state-of-the-art approaches that learn contextual information from text to improve the performance of several NLP tasks. These models, albeit powerful, still require specialized knowledge in specific scen
Externí odkaz:
http://arxiv.org/abs/2008.05190