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pro vyhledávání: '"LEWIS, ALAN"'
We present a local and transferable machine learning approach capable of predicting the real-space density response of both molecules and periodic systems to external homogeneous electric fields. The new method, SALTER, builds on the Symmetry-Adapted
Externí odkaz:
http://arxiv.org/abs/2304.09057
Autor:
Lewis, Alan, Miller, Tim
This paper investigates deceptive reinforcement learning for privacy preservation in model-free and continuous action space domains. In reinforcement learning, the reward function defines the agent's objective. In adversarial scenarios, an agent may
Externí odkaz:
http://arxiv.org/abs/2303.10838
Autor:
Goose, Nigel
Publikováno v:
The Economic History Review, 2007 Nov 01. 60(4), 830-832.
Externí odkaz:
https://www.jstor.org/stable/4502157
Autor:
George, Stuart
Publikováno v:
A Supplementary Dictionary of Sports Personalities, 1 ed., 2014.
The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to represent the sc
Externí odkaz:
http://arxiv.org/abs/2206.14087
Autor:
MURDOCH, BRIAN
Publikováno v:
Literature and Theology, 2012 Sep 01. 26(3), 343-343.
Externí odkaz:
http://dx.doi.org/10.1093/litthe/frs025
Autor:
Lewis, Alan L., Pirjol, Dan
We study the convergence properties of the short maturity expansion of option prices in the uncorrelated log-normal ($\beta=1$) SABR model. In this model the option time-value can be represented as an integral of the form $V(T) = \int_{0}^\infty e^{-
Externí odkaz:
http://arxiv.org/abs/2107.12439
Publikováno v:
Journal of Chemical Theory and Computation 2021, 17, 11, 7203-7214
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which enables an accurate expansion of the all-electron density in a form sui
Externí odkaz:
http://arxiv.org/abs/2106.05364