no code implementations • 1 Jul 2017 • A. H. Karimi, M. J. Shafiee, A. Ghodsi, A. Wong
In this work, we perform an exploratory study on synthesizing deep neural networks using biological synaptic strength distributions, and the potential influence of different distributions on modelling performance particularly for the scenario associated with small data sets.
no code implementations • 4 Feb 2016 • A. G. Chung, M. J. Shafiee, A. Wong
The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms.
no code implementations • 4 Feb 2016 • M. J. Shafiee, P. Siva, C. Scharfenberger, P. Fieguth, A. Wong
In this paper, a novel approach to visual salience detection via Neural Response Divergence (NeRD) is proposed, where synaptic portions of deep neural networks, previously trained for complex object recognition, are leveraged to compute low level cues that can be used to compute image region distinctiveness.