Yiran Chen | Akateeminen Kirjakauppa

Haullasi löytyi yhteensä 7 tuotetta
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LOW-POWER COMPUTER VISION - IMPROVE THE EFFICIENCY OF ARTIFICIAL INTELLIGENCE

George K. Thiruvathukal; Yung-Hsiang Lu; Jaeyoun Kim; Yiran Chen; Bo Chen
Taylor & Francis Ltd (2022)
Kovakantinen kirja

103,60 €

LOW-POWER COMPUTER VISION - IMPROVE THE EFFICIENCY OF ARTIFICIAL INTELLIGENCE

George K. Thiruvathukal; Yung-Hsiang Lu; Jaeyoun Kim; Yiran Chen; Bo Chen
Taylor & Francis Ltd (2024)
Pehmeäkantinen kirja

62,40 €

BIG CHINESE WORKBOOK FOR LITTLE HANDS, LEVEL 4 (AGES 9+)

Qin Chen; Yi Chen; Yiran Zheng
Independently Published (2021)
Pehmeäkantinen kirja

18,20 €

NONVOLATILE MEMORY DESIGN - MAGNETIC, RESISTIVE, AND PHASE CHANGE

Hai Li; Yiran Chen
Taylor & Francis Inc (2011)
Kovakantinen kirja

305,90 €

NONVOLATILE MEMORY DESIGN - MAGNETIC, RESISTIVE, AND PHASE CHANGE

Hai Li; Yiran Chen
Taylor & Francis Ltd (2017)
Pehmeäkantinen kirja

124,90 €

ARTIFICIAL INTELLIGENCE - FIRST CAAI INTERNATIONAL CONFERENCE, CICAI 2021, HANGZHOU, CHINA, JUNE 5–6, 2021, PROCEEDINGS, PART II

Lu Fang; Yiran Chen; Guangtao Zhai; Jane Wang; Ruiping Wang; Weisheng Dong
Springer Nature Switzerland AG (2021)
Pehmeäkantinen kirja

78,60 €

ARTIFICIAL INTELLIGENCE - FIRST CAAI INTERNATIONAL CONFERENCE, CICAI 2021, HANGZHOU, CHINA, JUNE 5–6, 2021, PROCEEDINGS, PART I

Lu Fang; Yiran Chen; Guangtao Zhai; Jane Wang; Ruiping Wang; Weisheng Dong
Springer Nature Switzerland AG (2021)
Pehmeäkantinen kirja

107,50 €

Low-Power Computer Vision - Improve the Efficiency of Artificial Intelligence

George K. Thiruvathukal, Yung-Hsiang Lu, Jaeyoun Kim, Yiran Chen, Bo Chen

103,60 €

Taylor & Francis Ltd
Sivumäärä: 436 sivua
Asu: Kovakantinen kirja
Julkaisuvuosi: 2022, 23.02.2022 (lisätietoa)
Kieli: Englanti

Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.

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