We present a novel action recognition method which is based on combining the effective description properties of Local Binary Patterns with the appearance invariance and adaptabil...
Abstract--In the Relational Reinforcement learning framework, we propose an algorithm that learns an action model allowing to predict the resulting state of each action in any give...
This research study was designed to broaden understanding of the publishing of research datasets by distinguishing between the intention to share and the action of sharing. The da...
We describe an incremental parser that was trained to minimize cost over sentences rather than over individual parsing actions. This is an attempt to use the advantages of the two...
We address two open theoretical questions in Policy Gradient Reinforcement Learning. The first concerns the efficacy of using function approximation to represent the state action ...