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pro vyhledávání: '"Hutchinson, Michael John"'
Autor:
Mostowsky, Peter, Dutordoir, Vincent, Azangulov, Iskander, Jaquier, Noémie, Hutchinson, Michael John, Ravuri, Aditya, Rozo, Leonel, Terenin, Alexander, Borovitskiy, Viacheslav
Kernels are a fundamental technical primitive in machine learning. In recent years, kernel-based methods such as Gaussian processes are becoming increasingly important in applications where quantifying uncertainty is of key interest. In settings that
Externí odkaz:
http://arxiv.org/abs/2407.08086
Autor:
Phillips, Angus, Dau, Hai-Dang, Hutchinson, Michael John, De Bortoli, Valentin, Deligiannidis, George, Doucet, Arnaud
Denoising diffusion models have become ubiquitous for generative modeling. The core idea is to transport the data distribution to a Gaussian by using a diffusion. Approximate samples from the data distribution are then obtained by estimating the time
Externí odkaz:
http://arxiv.org/abs/2402.06320
Autor:
Hutchinson, Michael John1, Valentino, Sydney Ella2, Totosy de Zepetnek, Julia1,2,3, MacDonald, Maureen Jane1,2, Goosey-Tolfrey, Victoria Louise1,3 V.L.Tolfrey@lboro.ac.uk
Publikováno v:
Applied Physiology, Nutrition & Metabolism. Feb2020, Vol. 45 Issue 2, p129-134. 6p. 2 Charts, 1 Graph.
Akademický článek
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Publikováno v:
Journal of Sports Sciences. 37:701-707
This study compares test-retest reliability and peak exercise responses from ramp-incremented (RAMP) and maximal perceptually-regulated (PRETmax) exercise tests during arm crank exercise in individuals reliant on manual wheelchair propulsion (MWP). T