Chris Eliasmith

Compositional neurosymbolic representations enable efficient active exploration

Autonomous systems that learn and explore over long horizons face a problem. Standard methods scale poorly in the number of observations, n, precluding sustained operation on bounded hardware. We show that compositional, high-dimensional vector representations inspired by neural computation address these constraints. W...