23 citations · 50 across the 9 of their papers we have counts for
10 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…
Learning abstract perceptual notions: the example of space
Alexander V. Terekhov, J. Kevin O'Regan
Humans are extremely swift learners. We are able to grasp highly abstract notions, whether they come from art perception or pure mathematics. Current machine learning techniques de…
Learning agent's spatial configuration from sensorimotor invariants
Alban Laflaquière, J. Kevin O'Regan, Sylvain Argentieri +2
The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world. This intuition has been proven incorrect with regard to a number of…
Learning an internal representation of the end-effector configuration space
Alban Laflaquière, Alexander V. Terekhov, Bruno Gas +1
Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or en…
Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience
Alban Laflaquière, J. Kevin O'Regan, Bruno Gas +1
In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in…
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…