Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Jiyan Yang"'
Publikováno v:
BMC Pregnancy and Childbirth, Vol 23, Iss 1, Pp 1-7 (2023)
Abstract Objective Not all infants with persistent pulmonary hypertension of the newborn (PPHN) respond to inhaled nitric oxide (iNO) therapy, as it is known to improve oxygenation in only 50% to 60% of cases. In this study, we investigated whether A
Externí odkaz:
https://doaj.org/article/56dba446f5cc4c72909fb7dc70df3c82
Publikováno v:
Frontiers in Neurology, Vol 14 (2023)
Externí odkaz:
https://doaj.org/article/5ed5b29e795345649585737ed3807ebe
Publikováno v:
Frontiers in Neurology, Vol 14 (2023)
BackgroundA deep learning (DL) model based on representative biopsy tissues can predict the recurrence and overall survival of patients with glioma, leading to optimized personalized medicine. This research aimed to develop a DL model based on hemato
Externí odkaz:
https://doaj.org/article/3d0a505f9b5847618c1c15ecfd60d2d6
Autor:
Bor-Yiing Su, Yuzhen Huang, Xiaohan Wei, Dhruv Choudhary, Xing Wang, Jiyan Yang, Zewei Jiang, Hai Zheng, Jack Langman, Shivam Bharuka
Publikováno v:
KDD
Neural network based recommendation models are widely used to power many internet-scale applications including product recommendation and feed ranking. As the models become more complex and more training data is required during training, improving th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e48ee97bb879bb471bb3741e2a338a3
https://doi.org/10.1145/3447548.3467084
https://doi.org/10.1145/3447548.3467084
Autor:
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie (Amy) Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, KR Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Ajit Mathews, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b1a6d07c5739fe931da1f91ac4f756a
http://arxiv.org/abs/2104.05158
http://arxiv.org/abs/2104.05158
Publikováno v:
KDD
Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural architecture search (NAS), as an emerging field, has demonstrated its ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47364eae02c7ae14756daf198d1c4a98
http://arxiv.org/abs/2007.06434
http://arxiv.org/abs/2007.06434
Publikováno v:
ISIT
Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive - potentially occupying hundreds of gigabytes in large-scale settings. To help manage this outsized memory consu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a27d4e51529e8ff4007bf50b6cf81ae
Publikováno v:
KDD
Modern deep learning-based recommendation systems exploit hundreds to thousands of different categorical features, each with millions of different categories ranging from clicks to posts. To respect the natural diversity within the categorical data,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5f653a5c6b3e8363a7d85adf26ba8c1
Publikováno v:
Proceedings of the IEEE. 104:58-92
In this era of large-scale data, distributed systems built on top of clusters of commodity hardware provide cheap and reliable storage and scalable processing of massive data. With cheap storage, instead of storing only currently relevant data, it is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57ed7f9a06dae15c4152ad6237dfd3f1
https://doi.org/10.1109/jproc.2015.2494219
https://doi.org/10.1109/jproc.2015.2494219
Publikováno v:
Proceedings of the ... Annual ACM-SIAM Symposium on Discrete Algorithms. ACM-SIAM Symposium on Discrete Algorithms. 2016
In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::184f6501212384a38fadb201cf60de14
https://pubmed.ncbi.nlm.nih.gov/29782626
https://pubmed.ncbi.nlm.nih.gov/29782626