Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Tzu-Kuo Huang"'
Autor:
Tzu-Kuo Huang, 黃子國
94
This thesis studies the problem of ranking individuals from their group competition results. Many real-world problems are of this type. For example, ranking players from team games is important in some sports. In machine learning, this is clo
This thesis studies the problem of ranking individuals from their group competition results. Many real-world problems are of this type. For example, ranking players from team games is important in some sports. In machine learning, this is clo
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/44714999094777724948
Autor:
Fang-Chieh Chou, Tzu-Kuo Huang, Vladan Radosavljevic, Nemanja Djuric, Matthew Niedoba, Tsung-Han Lin, Jeff Schneider, Henggang Cui, Thi Nguyen
Publikováno v:
2020 IEEE Intelligent Vehicles Symposium (IV).
Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment. This is particular
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ff508de1725ccd02c9850e95e7ccf2f
https://doi.org/10.1109/iv47402.2020.9304564
https://doi.org/10.1109/iv47402.2020.9304564
Publikováno v:
ITSC
Motion prediction of surrounding vehicles is one of the most important tasks handled by a self-driving vehicle, and represents a critical step in the autonomous system necessary to ensure safety for all the involved traffic actors. Recently a number
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e653d66cccfb4676d63123fcbc7fd43f
https://doi.org/10.1109/itsc45102.2020.9294553
https://doi.org/10.1109/itsc45102.2020.9294553
Publikováno v:
IEEE Security & Privacy. 16:34-42
Today’s companies collect immense amounts of personal data and enable wide access to it within the company. This exposes the data to external hackers and privacy-transgressing employees. This study shows that, for a wide and important class of work
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b7c25bdb0eea3db9192af875ac0406a3
https://doi.org/10.1109/msp.2018.1331036
https://doi.org/10.1109/msp.2018.1331036
Autor:
Vladan Radosavljevic, Nemanja Djuric, Tzu-Kuo Huang, Thi Nguyen, Tsung-Han Lin, Jeff Schneider, Fang-Chieh Chou, Henggang Cui
Publikováno v:
ICRA
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to preven
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a6a4500410ec4bb42bd34e586c31e1d
https://doi.org/10.1109/icra.2019.8793868
https://doi.org/10.1109/icra.2019.8793868
Publikováno v:
IEEE Symposium on Security and Privacy
Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. The ability to distinguish data that is actually needed from data collected "just in case" would help thes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2ee281a354a5d21bfe611409ee77c63
http://arxiv.org/abs/1705.07512
http://arxiv.org/abs/1705.07512
Autor:
Tzu-Kuo Huang
Virtually all methods of learning dynamic models from data start from the same basic assumption: that the learning algorithm will be provided with a single or multiple sequences of data generated from the dynamic model. However, in quite a few modern
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a8889d501aa8aaba3187ef518853ef1
Autor:
Tzu-Kuo Huang, Jeff Schneider
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783642334856
ECML/PKDD (2)
ECML/PKDD (2)
Vector Auto-regressive (VAR) models are useful for analyzing temporal dependencies among multivariate time series, known as Granger causality. There exist methods for learning sparse VAR models, leading directly to causal networks among the variables
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2de560b2eaf311f0a1749f7c9ec441d9
https://doi.org/10.1007/978-3-642-33486-3_47
https://doi.org/10.1007/978-3-642-33486-3_47
Publikováno v:
SDM
Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into acco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b745fcbb3fea20ee1cac6b42a3d8b47f
https://doi.org/10.1137/1.9781611972801.19
https://doi.org/10.1137/1.9781611972801.19
Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into acco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fe2b73504b52abeb22c3a560d0878fb