Zobrazeno 1 - 10
of 369
pro vyhledávání: '"Keegan E"'
Publikováno v:
AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist today. Today, machine learning algorithms, sometimes trained on alternati
Externí odkaz:
http://arxiv.org/abs/2210.02516
Publikováno v:
In Journal of Hydrology X 1 January 2025 26
Publikováno v:
In Biochemical and Biophysical Research Communications 30 August 2024 722
Autor:
Verma, Sahil, Boonsanong, Varich, Hoang, Minh, Hines, Keegan E., Dickerson, John P., Shah, Chirag
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine l
Externí odkaz:
http://arxiv.org/abs/2010.10596
Graph Representation Learning (GRL) has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks. Current approaches including shallow embeddings and Graph Neural Networks have mo
Externí odkaz:
http://arxiv.org/abs/2006.10252
With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties. While these approaches have improved performance in graph machine learning tasks compared to traditiona
Externí odkaz:
http://arxiv.org/abs/1910.03081
Autor:
Bruss, C. Bayan, Khazane, Anish, Rider, Jonathan, Serpe, Richard, Gogoglou, Antonia, Hines, Keegan E.
Financial transactions can be considered edges in a heterogeneous graph between entities sending money and entities receiving money. For financial institutions, such a graph is likely large (with millions or billions of edges) while also sparsely con
Externí odkaz:
http://arxiv.org/abs/1907.07225
Autor:
Bruss, C. Bayan, Khazane, Anish, Rider, Jonathan, Serpe, Richard, Nagrecha, Saurabh, Hines, Keegan E.
Graph embedding is a popular algorithmic approach for creating vector representations for individual vertices in networks. Training these algorithms at scale is important for creating embeddings that can be used for classification, ranking, recommend
Externí odkaz:
http://arxiv.org/abs/1907.01705
We present an end-to-end trainable multi-task network that addresses the problem of lexicon-free text extraction from complex documents. This network simultaneously solves the problems of text localization and text recognition and text segments are i
Externí odkaz:
http://arxiv.org/abs/1906.09266
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.