雖然這篇Pairwise rxjs鄉民發文沒有被收入到精華區：在Pairwise rxjs這個話題中，我們另外找到其它相關的精選爆讚文章
在 pairwise產品中有3篇Facebook貼文，粉絲數超過2,398的網紅DavidKo Learning Journey，也在其Facebook貼文中提到， Pairwise testing 最麻煩的地方, 是產生出來的組合, 必須要再檢查, 看看是否值得拿來測試. https://kojenchieh.pixnet.net/blog/post/485884478-pairwise-testing-%E9%80%99%E7%A8%AE%E7%B5%...
同時也有10000部Youtube影片，追蹤數超過2,910的網紅コバにゃんチャンネル，也在其Youtube影片中提到，...
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pairwise 在 コバにゃんチャンネル Youtube 的最佳貼文
2021-10-01 13:19:08![post-title]()
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pairwise 在 大象中醫 Youtube 的最讚貼文
2021-10-01 13:10:45![post-title]()
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pairwise 在 大象中醫 Youtube 的最讚貼文
2021-10-01 13:09:56![post-title]()
pairwise 在 DavidKo Learning Journey Facebook 的最讚貼文
Pairwise testing 最麻煩的地方, 是產生出來的組合, 必須要再檢查, 看看是否值得拿來測試.
https://kojenchieh.pixnet.net/blog/post/485884478-pairwise-testing-%E9%80%99%E7%A8%AE%E7%B5%84%E5%90%88%E6%B8%AC%E8%A9%A6%E6%8A%80%E5%A5%BD%E7%94%A8%E5%97%8E%3F
pairwise 在 DeepBelief.ai 深度學習 Facebook 的精選貼文
在過去深度學習在抽取視覺深度特徵任務通常有兩種手法一是標籤學習（softmax)，另一種則是基於正負樣本的成對學習（例如triplet loss)，曠視提出了新的理論將兩種方法統一，並提出了全新的circle loss(圓周損失），並在多種任務以及多種骨幹中全面獲得目前最佳效果
https://mp.weixin.qq.com/s/OmbnyV1a_YEErG_XfHVW6Q
pairwise 在 國立陽明交通大學電子工程學系及電子研究所 Facebook 的最佳貼文
【Talk】Clock Synchronization in Wireless Sensor Networks: from Traditional Estimation Theory to Distributed
###@@@ You are all invited to come. @@@###
Topic：Clock Synchronization in Wireless Sensor Networks: from Traditional Estimation Theory to Distributed
Time：December 22, 2017 ( Friday, 11：00AM~12：00PM)
Venue：R210, Engineering Building 4, NCTU
交通大學工程四館210室
Speaker：Prof. Yik-Chung Wu / The University of Hong Kong
Language：Lectured in English
Abstract: In this talk, we will review the advances of clock synchronization in wireless sensor network in the past few years. We will begin with the optimal clock synchronization algorithms in pairwise setting, in which maximum likelihood (ML) estimator from traditional estimation theory is the major tool. Then, we will discuss the more challenging networkwide synchronization, in which every node in the network needs to synchronize with each other. In this case, more powerful distributed signal processing techniques are required. In particular, we will illustrate how Belief Propagation (BP), distributed Kalman Filter (KF) and Alternating Direction Method of Multipliers (ADMM) method help in solving networkwide synchronization.
Bio: Yik-Chung Wu received the B.Eng. (EEE) degree in 1998 and the M.Phil. degree in 2001 from the University of Hong Kong (HKU). He received the Croucher Foundation scholarship in 2002 to study Ph.D. degree at Texas A&M University, College Station, and graduated in 2005. From August 2005 to August 2006, he was with the Thomson Corporate Research, Princeton, NJ, as a Member of Technical Staff. Since September 2006, he has been with HKU, currently as an Associate Professor. He has been a visiting scholar at Princeton University for the summers of 2011 and 2015. His research interests are in general area of signal processing, machine learning, and communication systems, and in particular distributed signal processing and robust optimization theories with applications to communication systems and smart grid. Dr. Wu served as an Editor for IEEE Communications Letters, is currently an Editor for IEEE Transactions on Communications and Journal of Communications and Networks.