Inspired by classic generative adversarial networks (GAN), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN’s discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. In...

In this paper, we investigate how to learn to control a group of cooperative agents with limited sensing capabilities such as robot swarms. The agents have only very basic sensor capabilities, yet in a group they can accomplish sophisticated tasks, such as distributed assembly or search and rescue tasks. Learning a policy for a group of agents is difficult due to distributed partial observabili...

This paper aims to find an algorithmic structure that affords to predict and explain the economical choice behaviour particularly under uncertainty(random policies) by manipulating the prevalent Actor-Critic learning method to comply with the requirements we have been entrusted ever since the field of neuroeconomics dawned on us. Whilst skimming some basics of neuroeconomics that might be relev...

Modeling policies in reproducing kernel Hilbert space (RKHS) renders policy gradient reinforcement learning algorithms non-parametric. As a result, the policies become very flexible and have a rich representational potential without a predefined set of features. However, their performances might be either non-covariant under reparameterization of the chosen kernel, or very sensitive to step-siz...

An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention by Huitian Lei Chair: Professor Susan A. Murphy Assistant Professor Ambuj Tewari Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative health interventions. An Adaptive Intervention (AI) personalizes the type, mode and...

This paper discusses a combination of two techniques for improving the recognition accuracy of on-line handwritten character recognition: committee classification and adaptation to the user. A novel adaptive committee structure, namely the Class-Confidence Critic Combination (CCCC) scheme, is presented and evaluated. It is shown to be able to improve significantly on its member classifiers. Als...

Our goal is to establish the conceptual foundations for using the computational power that is or will be available on computer systems. Much of the available computing power is wasted, however, if users have difficulty understanding and llsing the full potential of these systems. Too much attention in the past has been given to the technology of computer systems and not enough to the effects of...