Results 1 to 10 of about 3,999,832 (304)
Geometry from Information Geometry [PDF]
AIP Conference Proceedings, 2015 We use the method of maximum entropy to model physical space as a curved statistical manifold. It is then natural to use information geometry to explain the geometry of space.
Caticha, Ariel
core +3 more sources
Nature Reviews Physics, 2021 Real networks are finite metric spaces. Yet the geometry induced by shortest path distances in a network is definitely not its only geometry. Other forms of network geometry are the geometry of latent spaces underlying many networks, and the effective geometry induced by dynamical processes in networks.
Dmitri Krioukov +2 more
exaly +4 more sources
Efficient Geometry-aware 3D Generative Adversarial Networks [PDF]
Computer Vision and Pattern Recognition, 2021 Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge.
Eric Chan +11 more
semanticscholar +1 more source
Wiley Encyclopedia of Computer Science and Engineering, 2022 We compute the fundamental group of an algebraic link. To cite this article: O. Neto, P.C. Silva, C. R. Acad. Sci. Paris, Ser. I 340 (2005). 2004 Académie des sciences. Published by Elsevier SAS. All rights reserved.
David A. Cox
semanticscholar +1 more source
Geometry-enhanced molecular representation learning for property prediction [PDF]
Nature Machine Intelligence, 2021 Effective molecular representation learning is of great importance to facilitate molecular property prediction. Recent advances for molecular representation learning have shown great promise in applying graph neural networks to model molecules. Moreover,
Xiaomin Fang +8 more
semanticscholar +1 more source
Spin impurities, Wilson lines and semiclassics
Journal of High Energy Physics, 2022 We consider line defects with large quantum numbers in conformal field theories. First, we consider spin impurities, both for a free scalar triplet and in the Wilson-Fisher O(3) model.
Gabriel Cuomo +3 more
doaj +1 more source
Boundary conformal field theory at large charge
Journal of High Energy Physics, 2021 We study operators with large internal charge in boundary conformal field theories (BCFTs) with internal symmetries. Using the state-operator correspondence and the existence of a macroscopic limit, we find a non-trivial relation between the scaling ...
Gabriel Cuomo +2 more
doaj +1 more source
Localized magnetic field in the O(N) model
Journal of High Energy Physics, 2022 We consider the critical O(N) model in the presence of an external magnetic field localized in space. This setup can potentially be realized in quantum simulators and in some liquid mixtures.
Gabriel Cuomo +2 more
doaj +1 more source
Extremal correlators and random matrix theory
Journal of High Energy Physics, 2021 We study the correlation functions of Coulomb branch operators of four-dimensional N $$ \mathcal{N} $$ = 2 Superconformal Field Theories (SCFTs).
Alba Grassi +2 more
doaj +1 more source
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Numerous deep learning applications benefit from multitask learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between
Alex Kendall, Y. Gal, R. Cipolla
semanticscholar +1 more source