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pro vyhledávání: '"Wilson, James T."'
Autor:
Wilson, James T.
Bayesian optimization is a popular framework for efficiently finding high-quality solutions to difficult problems based on limited prior information. As a rule, these algorithms operate by iteratively choosing what to try next until some predefined b
Externí odkaz:
http://arxiv.org/abs/2402.16811
Autor:
Cosier, Lucas, Iordan, Rares, Zwane, Sicelukwanda, Franzese, Giovanni, Wilson, James T., Deisenroth, Marc Peter, Terenin, Alexander, Bekiroglu, Yasemin
Publikováno v:
Artificial Intelligence and Statistics, 2024
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and prevent
Externí odkaz:
http://arxiv.org/abs/2309.00854
Autor:
Wilson, James T., Borovitskiy, Viacheslav, Terenin, Alexander, Mostowsky, Peter, Deisenroth, Marc Peter
Publikováno v:
Journal of Machine Learning Research, 22(105):1-47, 2021
As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer. Monte Carlo methods act as a convenient bridge for connecting intractable mathematical expressions with actionable estimates via s
Externí odkaz:
http://arxiv.org/abs/2011.04026
Autor:
Wilson, James T., Borovitskiy, Viacheslav, Terenin, Alexander, Mostowsky, Peter, Deisenroth, Marc Peter
Publikováno v:
International Conference on Machine Learning, 2020
Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability to faithfully represent predictive uncertainty. These problems typically exist as parts of larger fram
Externí odkaz:
http://arxiv.org/abs/2002.09309
Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decisi
Externí odkaz:
http://arxiv.org/abs/1805.10196
Bayesian optimization is a sample-efficient approach to solving global optimization problems. Along with a surrogate model, this approach relies on theoretically motivated value heuristics (acquisition functions) to guide the search process. Maximizi
Externí odkaz:
http://arxiv.org/abs/1712.00424
Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes
Externí odkaz:
http://arxiv.org/abs/1506.04449
As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very
Externí odkaz:
http://arxiv.org/abs/1504.04788
Autor:
Townsley, Thomas D1,2, Wilson, James T2, Akers, Harrison2, Bryant, Timothy2, Cordova, Salvador3, Wallace, T L1,4, Durston, Kirk K5, Deweese, Joseph E2,6,7 jdeweese@fhu.edu
Publikováno v:
Bioinformatics Advances. 2022, Vol. 2 Issue 1, p1-7. 7p.
Publikováno v:
ILR Review. Apr79, Vol. 32 Issue 3, p312-326. 15p.