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pro vyhledávání: '"BORIN, EDSON"'
The widespread application of machine learning algorithms is a matter of increasing concern for the data privacy research community, and many have sought to develop privacy-preserving techniques for it. Among existing approaches, the homomorphic eval
Externí odkaz:
http://arxiv.org/abs/2403.20190
Publikováno v:
In Neurocomputing 28 December 2024 610
Dynamic Translation (DT) is a sophisticated technique that allows the implementation of high-performance emulators and high-level-language virtual machines. In this technique, the guest code is compiled dynamically at runtime. Consequently, achieving
Externí odkaz:
http://arxiv.org/abs/2008.13240
Autor:
Napoli, Otávio O., Rosario, Vanderson Martins do, Navarro, João Paulo, Silva, Pedro Mário Cruz e, Borin, Edson
Classification of seismic facies is done by clustering seismic data samples based on their attributes. Year after year, 3D datasets used by exploration geophysics increase in size, complexity, and number of attributes, requiring a continuous rise in
Externí odkaz:
http://arxiv.org/abs/2007.15152
The use of cloud computational resources has become increasingly important for companies and researchers to access on-demand and at any moment high-performance resources. However, given the wide variety of virtual machine types, network configuration
Externí odkaz:
http://arxiv.org/abs/2006.15481
Autor:
Pisani, Flavia, de Oliveira, Fabiola M. C., Gama, Eduardo S., Immich, Roger, Bittencourt, Luiz F., Borin, Edson
Publikováno v:
IOS Press Advances in Edge Computing: Massive Parallel Processing and Applications, Series Advances in Parallel Computing, Volume 35 (2020). ISBN978-1-64368-062-0 (print) | 978-1-64368-063-7 (online)
In the long term, the Internet of Things (IoT) is expected to become an integral part of people's daily lives. In light of this technological advancement, an ever-growing number of objects with limited hardware may become connected to the Internet. I
Externí odkaz:
http://arxiv.org/abs/2002.05300
Akademický článek
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Some Deep Neural Networks (DNN) have what we call lanes, or they can be reorganized as such. Lanes are paths in the network which are data-independent and typically learn different features or add resilience to the network. Given their data-independe
Externí odkaz:
http://arxiv.org/abs/1908.03935
We introduce Multi-Lane Capsule Networks (MLCN), which are a separable and resource efficient organization of Capsule Networks (CapsNet) that allows parallel processing, while achieving high accuracy at reduced cost. A MLCN is composed of a number of
Externí odkaz:
http://arxiv.org/abs/1902.08431