Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Konstantin Berestizshevsky"'
Publikováno v:
Journal of Communications and Networks. 23:326-339
We address the problem of controlling the COVID19 contagion with a limited number of PCR-tests. We developed a tool that can assist policy makers in decisions as well as in justifying these decisions. Our tool consists of: A stochastic disease model,
Publikováno v:
IEEE Embedded Systems Letters. 10:57-60
This letter presents a novel approach for designing a dynamic branch predictor. The proposed design, called decoupled-predictor literally decouples the prediction making from the prediction update stages of the scheme. This separation is intended to
Publikováno v:
Microprocessors and Microsystems. 50:138-153
We present a novel network-on-chip (NoC) architecture, called SDNoC, that is based on a hybrid hardware/software approach. This approach is based on a few principles used in Software defined networks (SDNs). In particular, the control network and the
The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::900c78d3439d997c176c8c241ad814fa
http://arxiv.org/abs/1910.06401
http://arxiv.org/abs/1910.06401
Autor:
Guy Even, Konstantin Berestizshevsky
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030304836
ICANN (2)
ICANN (2)
We study the tradeoff between computational effort and classification accuracy in a cascade of deep neural networks. During inference, the user sets the acceptable accuracy degradation which then automatically determines confidence thresholds for the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82c088f7cabf07595350b0966861ad56
https://doi.org/10.1007/978-3-030-30484-3_26
https://doi.org/10.1007/978-3-030-30484-3_26
Autor:
Guy Even, Konstantin Berestizshevsky
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030304836
ICANN (2)
ICANN (2)
We study the performance of stochastic gradient descent (SGD) in deep neural network (DNN) models. We show that during a single training epoch the signs of the partial derivatives of the loss with respect to a single parameter are distributed almost
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::051218b376fca8dff0abc9cbf1ed54fb
https://doi.org/10.1007/978-3-030-30484-3_18
https://doi.org/10.1007/978-3-030-30484-3_18