Improving Interpretability and Regularisation in Deep Learning
Repository URI
Repository DOI
Change log
Authors
Ragni, Anton
Karanasou, Penny
Sim, Khe Chai
Description
The provided .ctm and scoring .sys files correspond to the MPE systems of Table VI (Javanese) and Table X (BN) of this paper.
Version
Software / Usage instructions
HTK Toolkit, NIST SCLITE Scoring Package
Keywords
activation regularisation, interpretability, visualisation, neural network, deep learning
Publisher
Sponsorship
IARPA (4912046943)
EPSRC (via University of Edinburgh) (EP/I031022/1 ERI016379)
EPSRC (via University of Edinburgh) (EP/I031022/1 ERI016379)