Deep learning has exploded in the public consciousness, primarily as predictive and
analytical products suffuse our world, in the form of numerous human-centered smart-world …

We propose a self-supervised approach for learning representations of objects from
monocular videos and demonstrate it is particularly useful in situated settings such as …

This paper addresses the problem of object discovery from unlabeled driving videos
captured in a realistic automotive setting. Identifying recurring object categories in such raw …

We propose a self-supervised approach for learning representations of objects from
monocular videos and demonstrate it is particularly useful for robotics. The main …

In robotic applications, we often face the challenge of discovering new objects while having
very little or no labelled training data. In this paper we explore the use of self-supervision …

Obtaining hand-labeled training data is one of the most tedious and expensive parts of the
machine learning pipeline. Previous approaches, such as active learning aim at optimizing …

Agent-based modeling of multi-agent systems has enormous potential with applications in
modeling social, economic, medical and other application domains containing temporal …

Mona Nashaat Ali Elmowafy Page 1 Developing and Evaluating Algorithms for Fixing Omission
and Commission Errors in Structured Data by Mona Nashaat Ali Elmowafy A thesis submitted in …

Training a machine learning model (eg, a neural network model such as a convolutional
neural network (CNN) model) so that, when trained, the model can be utilized in processing …

Discovering objects and their attributes is of great importance for autonomous agents to
effectively operate in human environments. This task is particularly challenging due to the …