Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Kalyan Veeramachaneni"'
Publikováno v:
Neural Information Processing ISBN: 9783031301100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c7c6ccc9cc8020f8703d0a2e87f78ee0
https://doi.org/10.1007/978-3-031-30111-7_41
https://doi.org/10.1007/978-3-031-30111-7_41
Publikováno v:
Proceedings of the ACM on Human-Computer Interaction. 5:1-39
While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams. We describe challenges to scaling data scien
Anomaly detection on time series data is increasingly common across various industrial domains that monitor metrics in order to prevent potential accidents and economic losses. However, a scarcity of labeled data and ambiguous definitions of anomalie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af470ff39d225c119eeef9a0ff2c46c4
Autor:
Ignacio Arnaldo, Kalyan Veeramachaneni
Publikováno v:
ACM SIGKDD Explorations Newsletter. 21:39-47
Although there is a large corpus of research focused on using machine learning to detect cyber threats, the solutions presented are rarely actually adopted in the real world. In this paper, we discuss the challenges that currently limit the adoption
Autor:
Furui Cheng, Dongyu Liu, Huamin Qu, Yanna Lin, Alexandra Zytek, Fan Du, Haomin Li, Kalyan Veeramachaneni
Publikováno v:
IEEE transactions on visualization and computer graphics. 28(1)
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks. Although many ML models perform promisingly, issues with model transparency and interpretability limit their adoption in clinical pr
Publikováno v:
IJCAI
In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person's fitness for a particular opportunity. In this paper, we aim to accomplish approximate group fairness in a
Publikováno v:
CHI Extended Abstracts
As machine learning is applied to an increasingly large number of domains, the need for an effective way to explain its predictions grows apace. In the domain of child welfare screening, machine learning offers a promising method of consolidating the
Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts - who often have no expertise in ML or data science - are asked to use ML predictions to make high-stakes decisions. Multiple ML usabil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c01ebd08e5efb1f584b5ddcfdd00e89
http://arxiv.org/abs/2103.02071
http://arxiv.org/abs/2103.02071
Detecting anomalies in time-varying multivariate data is crucial in various industries for the predictive maintenance of equipment. Numerous machine learning (ML) algorithms have been proposed to support automated anomaly identification. However, a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a745988115d0428184d5d317945f066
Autor:
Alfredo Cuesta-Infante, Dongyu Liu, Kalyan Veeramachaneni, Sarah Alnegheimish, Alexander Geiger
Publikováno v:
IEEE BigData
Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, detecting anomalies in time series data is particularly challenging due t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c5074f8f31d7ebe3ad5eaf3f43171278
http://arxiv.org/abs/2009.07769
http://arxiv.org/abs/2009.07769