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pro vyhledávání: '"Tribes, Christophe"'
Autor:
Andrés-Thió, Nicolau, Audet, Charles, Diago, Miguel, Gheribi, Aimen E., Digabel, Sébastien Le, Lebeuf, Xavier, Garneau, Mathieu Lemyre, Tribes, Christophe
This work introduces solar, a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a concentrated solar power plant simulated by blackbox numerical models.
Externí odkaz:
http://arxiv.org/abs/2406.00140
The fine-tuning of Large Language Models (LLMs) has enabled them to recently achieve milestones in natural language processing applications. The emergence of ever larger LLMs has paved the way for more efficient fine-tuning methods. Among these, the
Externí odkaz:
http://arxiv.org/abs/2312.00949
Training time budget and size of the dataset are among the factors affecting the performance of a Deep Neural Network (DNN). This paper shows that Neural Architecture Search (NAS), Hyper Parameters Optimization (HPO), and Data Augmentation help DNNs
Externí odkaz:
http://arxiv.org/abs/2301.09264
Autor:
Lakhmiri, Dounia, Zolnouri, Mahdi, Nia, Vahid Partovi, Tribes, Christophe, Digabel, Sébastien Le
Deep neural networks are getting larger. Their implementation on edge and IoT devices becomes more challenging and moved the community to design lighter versions with similar performance. Standard automatic design tools such as \emph{reinforcement le
Externí odkaz:
http://arxiv.org/abs/2301.06641
NOMAD is software for optimizing blackbox problems. In continuous development since 2001, it constantly evolved with the integration of new algorithmic features published in scientific publications. These features are motivated by real applications e
Externí odkaz:
http://arxiv.org/abs/2104.11627
The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a new application, tuning a deep neural network is a tedious and time cons
Externí odkaz:
http://arxiv.org/abs/1907.01698
Publikováno v:
In Computers & Graphics October 2015 51:35-42
Akademický článek
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Autor:
Audet, Charles1 Charles.Audet@gerad.ca, Tribes, Christophe1 christophe.tribes@polymtl.ca
Publikováno v:
Computational Optimization & Applications. Nov2018, Vol. 71 Issue 2, p331-352. 22p.
Akademický článek
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