Sami Mollard

This paper proposes a recurrent neural network architecture that learns to trace curves and objects using multiscale, incremental grouping, showing generalization from short to longer curves and 2D-objects.

This paper trains a recurrent neural network with reinforcement learning to solve multi-step visual tasks, showing it can learn elemental operations and propagate enhanced activity between subroutines.