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pro vyhledávání: '"Keith G. Mills"'
Autor:
Keith G. Mills, Mohammad Salameh, Di Niu, Fred X. Han, Seyed Saeed Changiz Rezaei, Hengshuai Yao, Wei Lu, Shuo Lian, Shangling Jui
Publikováno v:
IEEE Access, Vol 9, Pp 110962-110974 (2021)
Recent developments in Neural Architecture Search (NAS) resort to training the supernet of a predefined search space with weight sharing to speed up architecture evaluation. These include random search schemes, as well as various schemes based on opt
Externí odkaz:
https://doaj.org/article/a1e28986bd9d431aa6d51fab7a428655
Autor:
Fred X. Han, Keith G. Mills, Fabian Chudak, Parsa Riahi, Mohammad Salameh, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu
Publikováno v:
Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) ISBN: 9781611977653
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::688d3ae01cb06ec4e3a6e3b1297bf33b
https://doi.org/10.1137/1.9781611977653.ch81
https://doi.org/10.1137/1.9781611977653.ch81
Autor:
Seyed Saeed Changiz Rezaei, Wei Lu, Di Niu, Fred X. Han, Jialin Zhang, Fabian Chudak, Shuo Lian, Shangling Jui, Keith G. Mills
Publikováno v:
CIKM
Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications. While recent literature has focused on designing networks to maximize accuracy, little work has been conducted to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::641d909ce238bcda78b0357c67018dc0
https://doi.org/10.1145/3459637.3481944
https://doi.org/10.1145/3459637.3481944
Autor:
Wei Lu, Fred X. Han, Seyed Saeed Changiz Rezaei, Keith G. Mills, Di Niu, Mohammad Salameh, Shuo Lian, Shangling Jui
Publikováno v:
IJCAI
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess. In this paper, we propose Generative Adversarial NAS (GA-NAS) with theoret
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54cf7e939271544d274a64bb100bbbbf
http://arxiv.org/abs/2105.09356
http://arxiv.org/abs/2105.09356
Autor:
Keith G. Mills, Linglong Kong, Seyed Saeed Changiz Rezaei, Shangling Jui, Wei Lu, Mohammad Salameh, Di Niu, Fred X. Han, Shuo Lian
Publikováno v:
CIKM
Neural architecture search (NAS) has achieved remarkable results in deep neural network design. Differentiable architecture search converts the search over discrete architectures into a hyperparameter optimization problem which can be solved by gradi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a758e5e861feacd3bd58f6eddec28eb