Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Anastasios Kyrillidis"'
Autor:
Tom Pan, Chen Dun, Shikai Jin, Mitchell D. Miller, Anastasios Kyrillidis, George N. Phillips Jr.
Publikováno v:
Structural Dynamics, Vol 11, Iss 4, Pp 044701-044701-14 (2024)
Determining the atomic-level structure of a protein has been a decades-long challenge. However, recent advances in transformers and related neural network architectures have enabled researchers to significantly improve solutions to this problem. Thes
Externí odkaz:
https://doaj.org/article/35ebbaec82dd44a9a6ced8b4de52544e
Publikováno v:
IUCrJ, Vol 10, Iss 4, Pp 487-496 (2023)
The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallogr
Externí odkaz:
https://doaj.org/article/5f4e3dbe014048a3be76cba849b381eb
Autor:
Nicolae Sapoval, Amirali Aghazadeh, Michael G. Nute, Dinler A. Antunes, Advait Balaji, Richard Baraniuk, C. J. Barberan, Ruth Dannenfelser, Chen Dun, Mohammadamin Edrisi, R. A. Leo Elworth, Bryce Kille, Anastasios Kyrillidis, Luay Nakhleh, Cameron R. Wolfe, Zhi Yan, Vicky Yao, Todd J. Treangen
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-12 (2022)
Deep learning has enabled advances in understanding biology. In this review, the authors outline advances, and limitations of deep learning in five broad areas and the future challenges for the biosciences.
Externí odkaz:
https://doaj.org/article/14e0a06b00c7444c9c738a631681c526
Publikováno v:
Photonics, Vol 10, Iss 2, p 116 (2023)
We propose a new quantum state reconstruction method that combines ideas from compressed sensing, non-convex optimization, and acceleration methods. The algorithm, called Momentum-Inspired Factored Gradient Descent (MiFGD), extends the applicability
Externí odkaz:
https://doaj.org/article/9caa9fe17f314739aa658cf04a391ded
Publikováno v:
IEEE Control Systems Letters. 7:199-204
We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix over a set of local machines. QST is the canonical procedure to characte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59264f6c83b4ecd4def13eb5709d8dbb
https://doi.org/10.1109/lcsys.2022.3186693
https://doi.org/10.1109/lcsys.2022.3186693
Publikováno v:
Proceedings of the VLDB Endowment. 15:1581-1590
Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, thus enabling reductions in training time. Distributed learning partitions models and data over many machines, allowing model and dataset s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bdfeef0fb5b490352a2c098487d7cf03
https://doi.org/10.14778/3529337.3529343
https://doi.org/10.14778/3529337.3529343
Publikováno v:
Mechanical Systems and Signal Processing. 189:110059
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f6f8dfe0378520ebb81eb3dd80be553
https://doi.org/10.1016/j.ymssp.2022.110059
https://doi.org/10.1016/j.ymssp.2022.110059
Centroid based clustering methods such as k-means, k-medoids and k-centers are heavily applied as a go-to tool in exploratory data analysis. In many cases, those methods are used to obtain representative centroids of the data manifold for visualizati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb725697b969d9e593a83e7d776932fe
Publikováno v:
CIKM
The double descent curve is one of the most intriguing properties of deep neural networks. It contrasts the classical bias-variance curve with the behavior of modern neural networks, occurring where the number of samples nears the number of parameter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0cf8b40327d01e76504a20db4483d3ee
http://arxiv.org/abs/2107.00797
http://arxiv.org/abs/2107.00797
Publikováno v:
Mathematics of Operations Research. 43:1326-1347
We propose a new proximal, path-following framework for a class of constrained convex problems. We consider settings where the nonlinear---and possibly non-smooth---objective part is endowed with a proximity operator, and the constraint set is equipp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::375c7f20c78b0c9618735244e6b57af3
https://doi.org/10.1287/moor.2017.0907
https://doi.org/10.1287/moor.2017.0907