TO LEARN OR NOT TO LEARN - HERE ARE THE REASONS

To Learn or Not to Learn ...

Multiagent systems in which agents interact with each other are now being proposed as a solution to many problems which can be grouped together under the \distributed problem solv-ing" umbrella. For such systems to work properly , it is necessary that agents learn from their environment and adapt their behaviour accordingly. In this paper we present a system which uses a combination of neuro{fu...

To Learn or Not do Learn

To Learn or Not to Learn Features for Deformable Registration?

Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question on how features learnt using various Deep Learning (DL) frameworks can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL met...

Examples are not enough, learn to criticize! Criticism for Interpretability

Example-based explanations are widely used in the effort to improve the interpretability of highly complex distributions. However, prototypes alone are rarely sufficient to represent the gist of the complexity. In order for users to construct better mental models and understand complex data distributions, we also need criticism to explain what are not captured by prototypes. Motivated by the Ba...

Bonus or Not? Learn to Reward in Crowdsourcing

Recent work has shown that the quality of work produced in a crowdsourcing working session can be influenced by the presence of performancecontingent financial incentives, such as bonuses for exceptional performance, in the session. We take an algorithmic approach to decide when to offer bonuses in a working session to improve the overall utility that a requester derives from the session. Speci...