Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Angelikopoulos, P."'
Autor:
Cherian, John J., Taube, Andrew G., McGibbon, Robert T., Angelikopoulos, Panagiotis, Blanc, Guy, Snarski, Michael, Richman, Daniel D., Klepeis, John L., Shaw, David E.
Identifying optimal values for a high-dimensional set of hyperparameters is a problem that has received growing attention given its importance to large-scale machine learning applications such as neural architecture search. Recently developed optimiz
Externí odkaz:
http://arxiv.org/abs/2008.06431
Autor:
Lipkova, Jana, Angelikopoulos, Panagiotis, Wu, Stephen, Alberts, Esther, Wiestler, Benedikt, Diehl, Christian, Preibisch, Christine, Pyka, Thomas, Combs, Stephanie, Hadjidoukas, Panagiotis, Van Leemput, Koen, Koumoutsakos, Petros, Lowengrub, John S., Menze, Bjoern
Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy
Externí odkaz:
http://arxiv.org/abs/1807.00499
Autor:
Kulakova, Lina, Arampatzis, Georgios, Angelikopoulos, Panagiotis, Chatzidoukas, Panagiotis, Papadimitriou, Costas, Koumoutsakos, Petros
The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The potential models atomistic attraction and repulsion with century old prescribed parameters (
Externí odkaz:
http://arxiv.org/abs/1705.08533
We classify two types of Hierarchical Bayesian Model found in the literature as Hierarchical Prior Model (HPM) and Hierarchical Stochastic Model (HSM). Then, we focus on studying the theoretical implications of the HSM. Using examples of polynomial f
Externí odkaz:
http://arxiv.org/abs/1611.02818
Autor:
Arampatzis, Georgios, Wälchli, Daniel, Angelikopoulos, Panagiotis, Wu, Stephen, Hadjidoukas, Panagiotis, Koumoutsakos, Petros
We propose an algorithm for the efficient and robust sampling of the posterior probability distribution in Bayesian inference problems. The algorithm combines the local search capabilities of the Manifold Metropolis Adjusted Langevin transition kerne
Externí odkaz:
http://arxiv.org/abs/1610.05660
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 January 2017 313:744-758
Publikováno v:
Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 2016 Feb . 374(2060), 1-23.
Externí odkaz:
http://www.jstor.org/stable/24758900
Publikováno v:
In Journal of Computational Physics 1 March 2015 284:1-21
Autor:
Hadjidoukas, P.E., Angelikopoulos, P., Rossinelli, D., Alexeev, D., Papadimitriou, C., Koumoutsakos, P.
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 December 2014 282:218-238
Publikováno v:
In Computer Physics Communications July 2014 185(7):2217-2219