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Socially Cognizant Robotics for a Technology Enhanced Society
Emerging applications of robotics, and concerns about their impact, require the research community to put human-centric objectives front-and-center.
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks
We focus on Convolutional Neural Networks (CNN) as the state-of-the-art models in object recognition and classification; investigate this problem in more detail, and hypothesize that training CNN models suffer from unstructured loss minimization.
Toward a Taxonomy and Computational Models of Abnormalities in Images
In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before.
A Bayesian Framework for Figure-Ground Interpretation
Figure/ground assignment, in which the visual image is divided into nearer (figural) and farther (ground) surfaces, is an essential step in visual processing, but its underlying computational mechanisms are poorly understood.
