111 citations · 150 across the 3 of their papers we have counts for
3 papers
quant-ph2023★ 1 cited
Using Variational Eigensolvers on Low-End Hardware to Find the Ground State Energy of Simple Molecules
T. Powers, R. M. Rajapakse
Key properties of physical systems can be described by the eigenvalues of matrices that represent the system. Computational algorithms that determine the eigenvalues of these matri…
stat.ML2016★ 38 cited
Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery
Scott Wisdom, Thomas Powers, James Pitton +1
Recurrent neural networks (RNNs) are powerful and effective for processing sequential data. However, RNNs are usually considered "black box" models whose internal structure and lea…
stat.ML2016★ 111 cited
Full-Capacity Unitary Recurrent Neural Networks
Scott Wisdom, Thomas Powers, John R. Hershey +2
Recurrent neural networks are powerful models for processing sequential data, but they are generally plagued by vanishing and exploding gradient problems. Unitary recurrent neural…