NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

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.

BIG-bench Machine Learning Chunking +2

Iterative Retraining of Quantum Spin Models Using Recurrent Neural Networks

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

Kernel RNN Learning (KeRNL)

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.