Zobrazeno 1 - 10
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pro vyhledávání: '"Sullivan T."'
Autor:
Aguilar-Arevalo, A., Aoki, M., Blecher, M., Britton, D. I., Bryman, D. A., Doria, L., Cuen-Rochin, S., Gumplinger, P., Hernandez, I., Hussein, A., Ito, S., Kurchaninov, L., Littenberg, L., Malbrunot, C., Mischke, R. E., Numao, T., Protopopescu, D., Sher, A., Sullivan, T., Vavilov, D.
Measurements of the response function of the PIENU NaI(T$\ell$) and CsI crystal calorimeter using a monochromatic 70 MeV/c positron beam at various incidence angles are described. The experimental setup and relevant physical processes involved were s
Externí odkaz:
http://arxiv.org/abs/2410.20559
Autoencoders have found widespread application, in both their original deterministic form and in their variational formulation (VAEs). In scientific applications it is often of interest to consider data that are comprised of functions; the same persp
Externí odkaz:
http://arxiv.org/abs/2408.01362
Publikováno v:
Journal of Chemical Physics 161(6):064112, 14pp., 2024
Atomistic simulations often rely on interatomic potentials to access greater time- and length- scales than those accessible to first principles methods such as density functional theory (DFT). However, since a parameterised potential typically cannot
Externí odkaz:
http://arxiv.org/abs/2402.15419
Autor:
Sullivan, T. J.
Publikováno v:
Real Analysis Exchange 49(2):377-388, 2024
Hille's theorem is a powerful classical result in vector measure theory. It asserts that the application of a closed, unbounded linear operator commutes with strong/Bochner integration of functions taking values in a Banach space. This note shows tha
Externí odkaz:
http://arxiv.org/abs/2401.01845
Autor:
Klebanov, Ilja, Sullivan, T. J.
Integration against, and hence sampling from, high-dimensional probability distributions is of essential importance in many application areas and has been an active research area for decades. One approach that has drawn increasing attention in recent
Externí odkaz:
http://arxiv.org/abs/2308.10081
Autor:
Klebanov, Ilja, Sullivan, T. J.
The last decade has seen many attempts to generalise the definition of modes, or MAP estimators, of a probability distribution $\mu$ on a space $X$ to the case that $\mu$ has no continuous Lebesgue density, and in particular to infinite-dimensional B
Externí odkaz:
http://arxiv.org/abs/2306.16278
Autor:
Matsumoto, Tadashi, Sullivan, T. J.
Publikováno v:
Analysis and Applications 22(3):619-633, 2024
Gaussian processes (GPs) are widely-used tools in spatial statistics and machine learning and the formulae for the mean function and covariance kernel of a GP $T u$ that is the image of another GP $u$ under a linear transformation $T$ acting on the s
Externí odkaz:
http://arxiv.org/abs/2305.03594
We consider the problem of learning a linear operator $\theta$ between two Hilbert spaces from empirical observations, which we interpret as least squares regression in infinite dimensions. We show that this goal can be reformulated as an inverse pro
Externí odkaz:
http://arxiv.org/abs/2211.08875
Stochastic parareal (SParareal) is a probabilistic variant of the popular parallel-in-time algorithm known as parareal. Similarly to parareal, it combines fine- and coarse-grained solutions to an ordinary differential equation (ODE) using a predictor
Externí odkaz:
http://arxiv.org/abs/2211.05496
Autor:
Lambley, Hefin, Sullivan, T. J.
Publikováno v:
SIAM/ASA J. Uncertain. Quantif. 11(4):1195--1224, 2023
It is often desirable to summarise a probability measure on a space $X$ in terms of a mode, or MAP estimator, i.e.\ a point of maximum probability. Such points can be rigorously defined using masses of metric balls in the small-radius limit. However,
Externí odkaz:
http://arxiv.org/abs/2209.11517