Zobrazeno 1 - 10
of 164
pro vyhledávání: '"Nhan H"'
Autor:
Nhan H. Nguyen, Joseph Michaud, Rene Mogollon, Huiting Zhang, Heidi Hargarten, Rachel Leisso, Carolina A. Torres, Loren Honaas, Stephen Ficklin
Publikováno v:
Plant Direct, Vol 8, Iss 10, Pp n/a-n/a (2024)
Abstract Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit‐quality attributes during storage. Therefore, it is typical to repeatedly evaluate fr
Externí odkaz:
https://doaj.org/article/f0706b17a7a543a9ac778114a0cc51ae
Autor:
Nhan H. Nguyen, Fiona Y. Glassman, Robert K. Dingman, Gautam N. Shenoy, Elizabeth A. Wohlfert, Jason G. Kay, Richard B. Bankert, Sathy V. Balu-Iyer
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Abstract The safety and efficacy of several life-saving therapeutic proteins are compromised due to their immunogenicity. Once a sustained immune response against a protein-based therapy is established, clinical options that are safe and cost-effecti
Externí odkaz:
https://doaj.org/article/1786baa1e47743e4a012fbcf4ceadcfd
Publikováno v:
Chemical Engineering Transactions, Vol 78 (2020)
High production costs are biggest obstacles for the commercialization of the lignocellulosic bioethanol. For an attempt to reduce the chemical consumption of the alkaline pretreatment process, the waste solution with high basicity was investigated to
Externí odkaz:
https://doaj.org/article/27a82c88696847fbbebeca8bfe219401
In recent years, a proliferation of methods were developed for cooperative multi-agent reinforcement learning (c-MARL). However, the robustness of c-MARL agents against adversarial attacks has been rarely explored. In this paper, we propose to evalua
Externí odkaz:
http://arxiv.org/abs/2202.03558
Publikováno v:
NeurIPs 2021
We develop two new algorithms, called, FedDR and asyncFedDR, for solving a fundamental nonconvex composite optimization problem in federated learning. Our algorithms rely on a novel combination between a nonconvex Douglas-Rachford splitting method, r
Externí odkaz:
http://arxiv.org/abs/2103.03452
Motivated by broad applications in reinforcement learning and machine learning, this paper considers the popular stochastic gradient descent (SGD) when the gradients of the underlying objective function are sampled from Markov processes. This Markov
Externí odkaz:
http://arxiv.org/abs/2003.10973
Autor:
Pham, Nhan H., Nguyen, Lam M., Phan, Dzung T., Nguyen, Phuong Ha, van Dijk, Marten, Tran-Dinh, Quoc
Publikováno v:
Proceedings of the International Conference on Artificial Intelligence and Statistics, PMLR 108:374-385, 2020
We propose a novel hybrid stochastic policy gradient estimator by combining an unbiased policy gradient estimator, the REINFORCE estimator, with another biased one, an adapted SARAH estimator for policy optimization. The hybrid policy gradient estima
Externí odkaz:
http://arxiv.org/abs/2003.00430
Publikováno v:
ICML 2020
We develop two new stochastic Gauss-Newton algorithms for solving a class of non-convex stochastic compositional optimization problems frequently arising in practice. We consider both the expectation and finite-sum settings under standard assumptions
Externí odkaz:
http://arxiv.org/abs/2002.07290
Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learning
Motivated by broad applications in machine learning, we study the popular accelerated stochastic gradient descent (ASGD) algorithm for solving (possibly nonconvex) optimization problems. We characterize the finite-time performance of this method when
Externí odkaz:
http://arxiv.org/abs/2002.02873
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