Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Supun Nakandala"'
Autor:
Jordan A. Carlson, Nicola D. Ridgers, Supun Nakandala, Rong Zablocki, Fatima Tuz-Zahra, John Bellettiere, Paul R. Hibbing, Chelsea Steel, Marta M. Jankowska, Dori E. Rosenberg, Mikael Anne Greenwood-Hickman, Jingjing Zou, Andrea Z. LaCroix, Arun Kumar, Loki Natarajan
Publikováno v:
International Journal of Behavioral Nutrition and Physical Activity, Vol 19, Iss 1, Pp 1-10 (2022)
Abstract Background Hip-worn accelerometer cut-points have poor validity for assessing children’s sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for the
Externí odkaz:
https://doaj.org/article/065d56627b414076942b6e2cf267a816
Publikováno v:
Proceedings of the VLDB Endowment. 14:2687-2690
Deep learning (DL) is revolutionizing many fields. However, there is a major bottleneck for the wide adoption of DL: the pain of model selection , which requires exploring a large config space of model architecture and training hyper-parameters befor
Autor:
Mikael Anne Greenwood-Hickman, Andrea Z. LaCroix, Loki Natarajan, Jordan A. Carlson, Jingjing Zou, Arun Kumar, Supun Nakandala, Fatima Tuz-Zahra, John Bellettiere, Dori E. Rosenberg, Marta M. Jankowska, Paul R. Hibbing
Publikováno v:
Medicine and science in sports and exercise, vol 53, iss 11
Medicine and Science in Sports and Exercise
Medicine and Science in Sports and Exercise
Supplemental digital content is available in the text.
Introduction Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inabilit
Introduction Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inabilit
Autor:
Supun Nakandala, Arun Kumar
Publikováno v:
Proceedings of the 2022 International Conference on Management of Data.
Publikováno v:
ACM Transactions on Database Systems. 45:1-42
Deep learning now offers state-of-the-art accuracy for many prediction tasks. A form of deep learning called deep convolutional neural networks (CNNs) are especially popular on image, video, and time series data. Due to its high computational cost, C
Autor:
Anuj Bhandar, Eroma Abeysinghe, Marlon Pierce, Marcus Christie, Supun Nakandala, Suresh Marru, Sudhakar Pamidighantam
Publikováno v:
Future Generation Computer Systems. 111:780-785
Establishing users’ identities and determining their permissions before they access research infrastructure resources are key features of science gateways. With many science gateways now relying on general purpose gateway platform services, the cha
Publikováno v:
ACM SIGMOD Record. 49:61-68
Deep Convolutional Neural Networks (CNNs) now match human accuracy in many image prediction tasks, resulting in a growing adoption in e-commerce, radiology, and other domains. Naturally, "explaining" CNN predictions is a key concern for many users. S
Publikováno v:
Proceedings of the VLDB Endowment. 13:2159-2173
Deep neural networks (deep nets) are revolutionizing many machine learning (ML) applications. But there is a major bottleneck to wider adoption: the pain and resource intensiveness of model selection. This empirical process involves exploring deep ne
Autor:
Jordan A. Carlson, Nicola D. Ridgers, Supun Nakandala, Rong Zablocki, Fatima Tuz-Zahra, John Bellettiere, Paul R. Hibbing, Chelsea Steel, Marta M. Jankowska, Dori E. Rosenberg, Mikael Anne Greenwood-Hickman, Jingjing Zou, Andrea Z. LaCroix, Arun Kumar, Loki Natarajan
Publikováno v:
The international journal of behavioral nutrition and physical activity, vol 19, iss 1
Background Hip-worn accelerometer cut-points have poor validity for assessing children’s sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monito
Autor:
Dori E. Rosenberg, Loki Natarajan, Andrea Z. LaCroix, Sheri J. Hartman, Jingjing Zou, Supun Nakandala, Arun Kumar, Marta M. Jankowska, Jordan A. Carlson, John Bellettiere, Fatima Tuz-Zahra
Publikováno v:
J Meas Phys Behav
Journal for the measurement of physical behaviour, vol 4, iss 2
Journal for the measurement of physical behaviour, vol 4, iss 2
Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” De