Zobrazeno 1 - 10
of 179
pro vyhledávání: '"Gibson, Stuart"'
Publikováno v:
In Utilities Policy October 2024 90
Publikováno v:
J. Phys.: Condens. Matter 33, 324002 (2021) [Special Issue on Machine Learning in Condensed Matter Physics]
We present a theoretical study of the potential of Principal Component Analysis to analyse magnetic diffuse neutron scattering data on quantum materials. To address this question, we simulate the scattering function $S\left(\mathbf{q}\right)$ for a m
Externí odkaz:
http://arxiv.org/abs/2011.08234
Autor:
Bai, Fangliang, Liu, Jinchao, Liu, Xiaojuan, Osadchy, Margarita, Wang, Chao, Gibson, Stuart J.
Recent work showed neural-network-based approaches to reconstructing images from compressively sensed measurements offer significant improvements in accuracy and signal compression. Such methods can dramatically boost the capability of computational
Externí odkaz:
http://arxiv.org/abs/2004.13173
Deep Learning shows very good performance when trained on large labeled data sets. The problem of training a deep net on a few or one sample per class requires a different learning approach which can generalize to unseen classes using only a few repr
Externí odkaz:
http://arxiv.org/abs/1808.07270
Convolutional neural networks (CNN) have been shown to provide a good solution for classification problems that utilize data obtained from vibrational spectroscopy. Moreover, CNNs are capable of identification from noisy spectra without the need for
Externí odkaz:
http://arxiv.org/abs/1806.09981
Autor:
Wang, Guoqing, Mididoddi, Chaitanya K, Bai, Fangliang, Gibson, Stuart, Su, Lei, Liu, Jinchao, Wang, Chao
An ultrafast single-pixel optical 2D imaging system using a single multimode fiber (MF) is proposed. The MF acted as the all-optical random pattern generator. Light with different wavelengths pass through a single MF will generator all-optical random
Externí odkaz:
http://arxiv.org/abs/1803.03061
Publikováno v:
In Utilities Policy December 2022 79
In this paper we present a new method for cystoid macular edema (CME) segmentation in retinal Optical Coherence Tomography (OCT) images, using a fully convolutional neural network (FCN) and a fully connected conditional random fields (dense CRFs). As
Externí odkaz:
http://arxiv.org/abs/1709.05324
Autor:
Liu, Jinchao, Osadchy, Margarita, Ashton, Lorna, Foster, Michael, Solomon, Christopher J., Gibson, Stuart J.
Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline correction and/or PCA as an es
Externí odkaz:
http://arxiv.org/abs/1708.09022
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