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Full-Text Articles in Physical Sciences and Mathematics
Federated Learning In Gaze Recognition (Fligr), Arun Gopal Govindaswamy
Federated Learning In Gaze Recognition (Fligr), Arun Gopal Govindaswamy
College of Computing and Digital Media Dissertations
The efficiency and generalizability of a deep learning model is based on the amount and diversity of training data. Although huge amounts of data are being collected, these data are not stored in centralized servers for further data processing. It is often infeasible to collect and share data in centralized servers due to various medical data regulations. This need for diversely distributed data and infeasible storage solutions calls for Federated Learning (FL). FL is a clever way of utilizing privately stored data in model building without the need for data sharing. The idea is to train several different models locally …
Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen
Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen
Doctoral Dissertations
Recent advances in machine learning have allowed information retrieval (IR) techniques to advance beyond the stage of handcrafting domain specific features. Specifically, deep neural models incorporate varying levels of features to learn whether a document answers the information need of a query. However, these neural models rely on a large number of parameters to successfully learn a relation between a query and a relevant document.
This reliance on a large number of parameters, combined with the current methods of optimization relying on small updates necessitates numerous samples to allow the neural model to converge on an effective relevance function. This …
Single And Differential Morph Attack Detection, Baaria Chaudhary
Single And Differential Morph Attack Detection, Baaria Chaudhary
Graduate Theses, Dissertations, and Problem Reports
Face recognition systems operate on the assumption that a person's face serves as the unique link to their identity. In this thesis, we explore the problem of morph attacks, which have become a viable threat to face verification scenarios precisely because of their inherent ability to break this unique link. A morph attack occurs when two people who share similar facial features morph their faces together such that the resulting face image is recognized as either of two contributing individuals. Morphs inherit enough visual features from both individuals that both humans and automatic algorithms confuse them. The contributions of this …