7 citations · 12 across the 4 of their papers we have counts for
5 papers
Knowledge transfer in deep block-modular neural networks
Alexander V. Terekhov, Guglielmo Montone, J. Kevin O'Regan
Although deep neural networks (DNNs) have demonstrated impressive results during the last decade, they remain highly specialized tools, which are trained -- often from scratch -- t…
Why early tactile speech aids may have failed: no perceptual integration of tactile and auditory signals
Aurora Rizza, Alexander V. Terekhov, Guglielmo Montone +2
Tactile speech aids, though extensively studied in the 1980s and 90s, never became a commercial success. A hypothesis to explain this failure might be that it is difficult to obtai…
Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task
Guglielmo Montone, J. Kevin O'Regan, Alexander V. Terekhov
In the present work we propose a Deep Feed Forward network architecture which can be trained according to a sequential learning paradigm, where tasks of increasing difficulty are l…
Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end
Guglielmo Montone, J. Kevin O'Regan, Alexander V. Terekhov
In this work we propose a system for visual question answering. Our architecture is composed of two parts, the first part creates the logical knowledge base given the image. The se…
Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network
Guglielmo Montone, J. Kevin O'Regan, Alexander V. Terekhov
In this paper we present an alternative strategy for fine-tuning the parameters of a network. We named the technique Gradual Tuning. Once trained on a first task, the network is fi…