Code for paper "Full-Capacity Unitary Recurrent Neural Networks." Based on the complex_RNN repository from github.com/amarshah/complex_RNN.
Code coming soon for other experiments.
If you find this code useful, please cite the following references:
 M. Arjovsky, A. Shah, and Y. Bengio, “Unitary Evolution Recurrent Neural Networks,” Proc. International Conference on Machine Learning (ICML), 2016, pp. 1120–1128.
 S. Wisdom, T. Powers, J.R. Hershey, J. Le Roux, and L. Atlas, "Full-Capacity Unitary Recurrent Neural Networks," Advances in Neural Information Processing Systems (NIPS), 2016.
Instructions for TIMIT prediction experiment
Downsample the TIMIT dataset to 8ksamples/sec using Matlab by running
matlabdirectory. Make sure you modify the paths in
downsample_audio.mfor your system.
Download Matlab evaluation code using
download_and_unzip_matlab_code.py, which should download and unzip all the required toolboxes to the
Run the experiments using the shell scripts:
run_timit_prediction_<model>.sh, which will train the model and score the resulting audio using the Matlab evaluation toolboxes.