1 code implementation • 20 Dec 2021 • Filippo Vicentini, Damian Hofmann, Attila Szabó, Dian Wu, Christopher Roth, Clemens Giuliani, Gabriel Pescia, Jannes Nys, Vladimir Vargas-Calderon, Nikita Astrakhantsev, Giuseppe Carleo
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics.
1 code implementation • 9 Mar 2020 • Christopher Roth
Modeling quantum many-body systems is enormously challenging due to the exponential scaling of Hilbert dimension with system size.
Computational Physics Disordered Systems and Neural Networks Strongly Correlated Electrons
no code implementations • ICLR 2019 • Christopher Roth, Ingmar Kanitscheider, Ila Fiete
We describe Kernel RNN Learning (KeRNL), a reduced-rank, temporal eligibility trace-based approximation to backpropagation through time (BPTT) for training recurrent neural networks (RNNs) that gives competitive performance to BPTT on long time-dependence tasks.