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Design and evaluation of a hybrid multi-task learning model for optimizing deep reinforcement learning agents
(2021-04-01)
Driven by recent technological advancements within the artificial intelligence domain, deep learning has emerged as a promising representation learning technique. This in turn has given rise to the evolution of deep ...
Yield estimation and smart harvesting for precision agriculture using deep learning
(2021-08-01)
Precision agriculture is one of the fastest growing fields in recent years. In this thesis, we introduce a framework that provides farmers with a yield estimation from videos of crops and provides guided assistance for ...
Group representation learning for group recommendation
(2021-01-01)
Group recommender systems facilitate group decision making for a set of individuals (e.g., a group of friends, a team, a corporation, etc.). Existing group recommendation methods mostly learn group members' individual ...
Enhanced knowledge distillation by auxiliary classifiers
(2021-08-01)
Deep neural models have shown promising results in various areas, e.g., computer vision and natural language processing, at the cost of high computation and storage resource consumption. These characteristics of deep neural ...
Deep learning for islanding detection of grid-connected photovoltaic systems
(2021-01-01)
Over the past decade, the integration of renewable-based distribution energy resources within the smart distribution system has been steadily growing. Despite the numerous advantages of integrating these renewable energy ...
Developing a mobile manipulation system to handle unknown and unstructured objects
(2021-04-01)
The exceptional human’s ability to interact with unknown objects based on minimal prior experience is a permanent inspiration to the field of robotic manipulation. The recent revolution in industrial and service robots ...
On deep learning in physics
(2021-04-01)
Machine learning, and most notably deep neural networks, have seen unprecedented success in recent years due to their ability to learn complex nonlinear mappings by ingesting large amounts of data through the process of ...