Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Ayed, Ibrahim"'
Greedy layer-wise or module-wise training of neural networks is compelling in constrained and on-device settings where memory is limited, as it circumvents a number of problems of end-to-end back-propagation. However, it suffers from a stagnation pro
Externí odkaz:
http://arxiv.org/abs/2309.17357
End-to-end backpropagation has a few shortcomings: it requires loading the entire model during training, which can be impossible in constrained settings, and suffers from three locking problems (forward locking, update locking and backward locking),
Externí odkaz:
http://arxiv.org/abs/2210.00949
Autor:
Franceschi, Jean-Yves, de Bézenac, Emmanuel, Ayed, Ibrahim, Chen, Mickaël, Lamprier, Sylvain, Gallinari, Patrick
Publikováno v:
39th International Conference on Machine Learning, International Machine Learning Society, Jul 2022, Baltimore, MD, United States. pp.6660-6704
We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs). We reveal a fundamental flaw of previous analyses which, by incorrectly modeling GANs' training scheme, are subject to ill-defined discriminator gradient
Externí odkaz:
http://arxiv.org/abs/2106.05566
When modeling dynamical systems from real-world data samples, the distribution of data often changes according to the environment in which they are captured, and the dynamics of the system itself vary from one environment to another. Generalizing acr
Externí odkaz:
http://arxiv.org/abs/2106.04546
Autor:
Yin, Yuan, Guen, Vincent Le, Dona, Jérémie, de Bézenac, Emmanuel, Ayed, Ibrahim, Thome, Nicolas, Gallinari, Patrick
Publikováno v:
J. Stat. Mech. (2021) 124012
Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, st
Externí odkaz:
http://arxiv.org/abs/2010.04456
Neural networks have been achieving high generalization performance on many tasks despite being highly over-parameterized. Since classical statistical learning theory struggles to explain this behavior, much effort has recently been focused on uncove
Externí odkaz:
http://arxiv.org/abs/2009.08372
Domain Translation is the problem of finding a meaningful correspondence between two domains. Since in a majority of settings paired supervision is not available, much work focuses on Unsupervised Domain Translation (UDT) where data samples from each
Externí odkaz:
http://arxiv.org/abs/1906.01292
We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state. We propose a natural data-driven framework, where the system's dynamics are modelled by an unk
Externí odkaz:
http://arxiv.org/abs/1902.11136
Publikováno v:
Studies in English Language and Education, Vol 10, Iss 1, Pp 388-402 (2023)
This article investigates the trends in using commissive speech in Sulha proceedings in Jordan. Sulha focuses on a dispute-resolution system in Arab society that uses the Bedouin Arabic dialect as the primary language of communication. Qualitative an
Externí odkaz:
https://doaj.org/article/187f9d18d2f3447fbdadb1f9d6123462
Autor:
Al-Ayed, Ibrahim H.1 ialiyed@ksu.edu.sa, alharbi, Seba mohammed1, Alzanbagi, Mashael Abdullah1, Alqahtani, Sadeem Mesfer1, Alharbi, Abdulmalik Abdulaziz1
Publikováno v:
Journal of Pioneering Medical Sciences. Feb2024, Vol. 13 Issue 1, p42-46. 5p.