Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Christian R, Shelton"'
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045014 (2023)
We present the first multi-fidelity Bayesian optimization (BO) approach for solving inverse problems in the quantum control of prototypical quantum systems. Our approach automatically constructs time-dependent control fields that enable transitions b
Externí odkaz:
https://doaj.org/article/1649013c5ae346dab23693865fd95f2f
Publikováno v:
Frontiers in Psychology, Vol 11 (2020)
A key need in cognitive training interventions is to personalize task difficulty to each user and to adapt this difficulty to continually apply appropriate challenges as users improve their skill to perform the tasks. Here we examine how Bayesian fil
Externí odkaz:
https://doaj.org/article/54890a82d23441dd9aa3cbc3ced20ce0
Publikováno v:
Physical Chemistry Chemical Physics. 22:22889-22899
Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-depe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab50bb838aec0d384271fb403431ff09
https://doi.org/10.1039/d0cp03694c
https://doi.org/10.1039/d0cp03694c
Autor:
Dave Gomboc, Christian R. Shelton
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031114878
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::432050215d29ff978e4722a801b50fe9
https://doi.org/10.1007/978-3-031-11488-5_14
https://doi.org/10.1007/978-3-031-11488-5_14
Publikováno v:
2021 IEEE Real-Time Systems Symposium (RTSS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd92196bda13659badd7b31693a30e28
https://doi.org/10.1109/rtss52674.2021.00028
https://doi.org/10.1109/rtss52674.2021.00028
Autor:
Christian R. Shelton, Mariam Salloum, Vassilis J. Tsotras, Vagelis Papalexakis, Daniel R. Jeske, Shuheng Zhou, Wenxiu Ma
Publikováno v:
SIGCSE
This paper describes a newly developed Data Science major that is jointly offered between two departments (Computer Science and Statistics) and across two colleges (College of Engineering and College of Natural and Architectural Sciences). The paper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8e538084bbbb28648a584f26a218ed7
https://doi.org/10.1145/3408877.3432454
https://doi.org/10.1145/3408877.3432454
We present methods for emulating the matter power spectrum by combining information from cosmological $N$-body simulations at different resolutions. An emulator allows estimation of simulation output by interpolating across the parameter space of a l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::271c903d90cabaa562abe67cbf78dca6
Autor:
Christian R. Shelton, Mike Izbicki
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030438227
PKDD/ECML Workshops (1)
PKDD/ECML Workshops (1)
The optimal weighted average (OWA) is an algorithm for distributed learning of linear models. It achieves statistically optimal theoretical guarantees with only a single round of communication [3]. This paper introduces the non-linear OWA (NOWA) algo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09ad328bad237a6d236c2af54c37be1a
https://doi.org/10.1007/978-3-030-43823-4_25
https://doi.org/10.1007/978-3-030-43823-4_25
Autor:
Mike Izbicki, Christian R. Shelton
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030461461
ECML/PKDD (2)
ECML/PKDD (2)
We present the optimal weighted average (OWA) distributed learning algorithm for linear models. OWA achieves statistically optimal learning rates, uses only one round of communication, works on non-convex problems, and supports a fast cross validatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::158624fb2df6db624182f3c7958ece25
https://doi.org/10.1007/978-3-030-46147-8_12
https://doi.org/10.1007/978-3-030-46147-8_12
We develop an automated technique to measure quasar redshifts in the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey (SDSS). Our technique is an extension of an earlier Gaussian process method for detecting damped Lyman
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f07fb0e552ccac6d27bd1fbb1cb959c9