Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Krefl, Daniel"'
In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings. In detail, we proposed a pipeline that dynamically gathers contextual information fr
Externí odkaz:
http://arxiv.org/abs/2312.01954
The estimation of probability density functions is a non trivial task that over the last years has been tackled with machine learning techniques. Successful applications can be obtained using models inspired by the Boltzmann machine (BM) architecture
Externí odkaz:
http://arxiv.org/abs/2303.05910
Autor:
Tomasoni, Mattia, Beyeler, Michael Johannes, Vela, Sofia Ortin, Mounier, Ninon, Porcu, Eleonora, Corre, Tanguy, Krefl, Daniel, Button, Alexander Luke, Abouzeid, Hana, Lazaros, Konstantinidis, Bochud, Murielle, Schlingemann, Reinier, Bergin, Ciara, Bergmann, Sven
Publikováno v:
In Ophthalmology Science September 2023 3(3)
The probability density function for the visible sector of a Riemann-Theta Boltzmann machine can be taken conditional on a subset of the visible units. We derive that the corresponding conditional density function is given by a reparameterization of
Externí odkaz:
http://arxiv.org/abs/1905.11313
Autor:
Carrazza, Stefano, Krefl, Daniel
We show that the visible sector probability density function of the Riemann-Theta Boltzmann machine corresponds to a gaussian mixture model consisting of an infinite number of component multi-variate gaussians. The weights of the mixture are given by
Externí odkaz:
http://arxiv.org/abs/1804.07768
A general Boltzmann machine with continuous visible and discrete integer valued hidden states is introduced. Under mild assumptions about the connection matrices, the probability density function of the visible units can be solved for analytically, y
Externí odkaz:
http://arxiv.org/abs/1712.07581
Autor:
Krefl, Daniel, Seong, Rak-Kyeong
Publikováno v:
Phys. Rev. D 96, 066014 (2017)
We employ machine learning techniques to investigate the volume minimum of Sasaki-Einstein base manifolds of non-compact toric Calabi-Yau 3-folds. We find that the minimum volume can be approximated via a second order multiple linear regression on st
Externí odkaz:
http://arxiv.org/abs/1706.03346
Autor:
Krefl, Daniel
Publikováno v:
Journal of High Energy Physics, 2016(8), 1-28
The Nekrasov-Shatashvili limit of the refined topological string on toric Calabi-Yau manifolds and the resulting quantum geometry is studied from a non-perturbative perspective. The quantum differential and thus the quantum periods exhibit Stokes phe
Externí odkaz:
http://arxiv.org/abs/1605.00182
Autor:
Krefl, Daniel
We invoke integrals of Mellin-Barnes type to analytically continue the Gopakumar-Vafa resummation of the topological string free energy in the string coupling constant, leading to additional non-perturbative terms. We also discuss in a similar manner
Externí odkaz:
http://arxiv.org/abs/1508.04219