Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Zhanhong Jiang"'
Publikováno v:
Frontiers in Artificial Intelligence, Vol 4 (2021)
In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality. In this paper, we build on recent algorithmic progresses in distributed deep learning
Externí odkaz:
https://doaj.org/article/38e24df48695476585bd17ff21e1f8ab
Autor:
Zhanhong Jiang1, Risbeck, Michael J.2, Ramamurti, Vish3, Murugesan, Sugumar4, Amores, Jaume4, Lee, Young M.5, Drees, Kirk H.6
Publikováno v:
ASHRAE Transactions. 2021, Vol. 127 Issue 1, p38-47. 10p.
Autor:
Michael J. Risbeck, Alexander E. Cohen, Jonathan D. Douglas, Zhanhong Jiang, Carlo Fanone, Karen Bowes, Jim Doughty, Martin Turnbull, Louis DiBerardinis, Young M. Lee, Martin Z. Bazant
The global devastation of the COVID-19 pandemic has led to calls for a revolution in heating, ventilation, and air conditioning (HVAC) systems to improve indoor air quality (IAQ), due to the dominant role of airborne transmission in disease spread. W
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c09304731b67184664413cb4a888bb2
https://doi.org/10.1101/2023.03.19.23287460
https://doi.org/10.1101/2023.03.19.23287460
Regret has been widely adopted as the metric of choice for evaluating the performance of online optimization algorithms for distributed, multi-agent systems. However, data/model variations associated with agents can significantly impact decisions and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4347c81f26e3b4400dc718692c9b386
http://arxiv.org/abs/2209.10105
http://arxiv.org/abs/2209.10105
Autor:
Zhanhong Jiang, Michael J. Risbeck, Santle Camilas Kulandai Samy, Chenlu Zhang, Saman Cyrus, Young M. Lee
Publikováno v:
Energy and Buildings. 285:112876
Autor:
Martin Z. Bazant, Michael J. Risbeck, Zhanhong Jiang, Jonathan D. Douglas, Lee Young M, Kirk H. Drees
The COVID-19 pandemic has focused renewed attention on the ways in which building HVAC systems may be operated to mitigate the risk of airborne disease transmission. The most common suggestion is to increase outdoor-air ventilation rates so as to dil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05f845deb55e88dd98fd70bb4341d9a6
https://doi.org/10.1101/2021.11.15.21266233
https://doi.org/10.1101/2021.11.15.21266233
Publikováno v:
Frontiers in Artificial Intelligence
Frontiers in Artificial Intelligence, Vol 4 (2021)
Frontiers in Artificial Intelligence, Vol 4 (2021)
In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality. In this paper, we build on recent algorithmic progresses in distributed deep learning
Autor:
Zhanhong Jiang, Michael J. Risbeck, Jonathan D. Douglas, Lee Young M, Kirk H. Drees, Martin Z. Bazant
Publikováno v:
Energy and Buildings
The COVID-19 pandemic has renewed interest in assessing how the operation of HVAC systems influences the risk of airborne disease transmission in buildings. Various processes, such as ventilation and filtration, have been shown to reduce the probabil
Autor:
Zhanhong Jiang, Martin Z. Bazant, Michael J. Risbeck, Lee Young M, Kirk H. Drees, Jonathan D. Douglas
Since the advent of the COVID-19 pandemic, there has been renewed interest in determining how the operation of building HVAC systems influences the risk of airborne transmission of disease. It has been established that combinations of increased venti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d27850fa159f504219902052919a391
https://doi.org/10.1101/2021.06.21.21259287
https://doi.org/10.1101/2021.06.21.21259287