Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning

3 March 2020

Papers citing "Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning"

8 / 8 papers shown

Title
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows Mathis Gerdes P. D. Haan Corrado Rainone Roberto Bondesan Miranda C. N. Cheng AI4CE 24 40 0 01 Jul 2022
Stochastic normalizing flows as non-equilibrium transformations M. Caselle E. Cellini A. Nada M. Panero 36 34 0 21 Jan 2022
Machine Learning in Nuclear Physics A. Boehnlein M. Diefenthaler C. Fanelli M. Hjorth-Jensen T. Horn ... M. Schram A. Scheinker Michael S. Smith Xin-Nian Wang Veronique Ziegler AI4CE 47 41 0 04 Dec 2021
Quantum field-theoretic machine learning Dimitrios Bachtis Gert Aarts B. Lucini AI4CE 19 28 0 18 Feb 2021
Disentangling a Deep Learned Volume Formula J. Craven Vishnu Jejjala Arjun Kar 31 19 0 07 Dec 2020
Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data Cole Miles A. Bohrdt Ruihan Wu C. Chiu Muqing Xu G. Ji M. Greiner Kilian Q. Weinberger E. Demler Eun-Ah Kim 29 42 0 06 Nov 2020
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks S. J. Wetzel R. Melko Joseph Scott Maysum Panju Vijay Ganesh 33 64 0 09 Mar 2020
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,242 0 24 Jun 2017