no code implementations • 4 Feb 2021 • Veljko Dubljević, George F. List, Jovan Milojevich, Nirav Ajmeri, William Bauer, Munindar P. Singh, Eleni Bardaka, Thomas Birkland, Charles Edwards, Roger Mayer, Ioan Muntean, Thomas Powers, Hesham Rakha, Vance Ricks, M. Shoaib Samandar
There is a pressing need to address relevant social concerns to allow for the development of systems of intelligent agents that are informed and cognizant of ethical standards.
Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization.
This interpretability also provides principled initializations that enable faster training and convergence to better solutions compared to conventional random initialization.
To address this question, we propose full-capacity uRNNs that optimize their recurrence matrix over all unitary matrices, leading to significantly improved performance over uRNNs that use a restricted-capacity recurrence matrix.
Ranked #22 on Sequential Image Classification on Sequential MNIST
Our approach centers around using a single-channel minimum mean-square error log-spectral amplitude (MMSE-LSA) estimator proposed by Habets, which scales coefficients in a time-frequency domain to suppress noise and reverberation.