PhD Thesis: Geometry and Uncertainty in Deep Learning for.
The introduction of Deep learning has been instrumental in advanced capabilities and has taken the field of machine learning a leap forward. It is a type of Artificial Intelligence, where it imitates the process of the human brain for carrying out the computations and generating patterns for making decisions. Hence, it is also known as Deep Neural network. It is an unsupervised learning system.
At the doctoral level, students can typically pursue their Doctor of Philosophy (PhD) in Machine Learning or other related areas, like a PhD in Statistics with a track in machine learning and big.
The thesis also explores how to make deep learning models more computational-efficient by pruning unnecessary parameters. Many studies have shown that most of the computations occurred within convolutional layers, which are widely used in convolutional neural networks (CNNs) for many computer vision tasks. We designed a novel D-Pruner algorithm that allows us to score the parameters based on.
Ph.D. Thesis - Scalable Human Identification with Deep Learning human-identification person-reidentification person-search deep-learning thesis-template computer-vision 42 commits.
Deep Neural Network acoustic models for ASR Abdel-rahman Mohamed Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2014 Automatic speech recognition (ASR) is a key core technology for the information age. ASR systems have evolved from discriminating among isolated digits to recognizing telephone-quality, spontaneous speech, allowing for a growing number of.
The thesis “Content-Based Image Retrieval using Deep Learning” by Anshuman Vikram Singh has been examined and approved by the following Examination Committee: Dr. Roger S. Gaborski Professor Thesis Committee Chair Prof. Thomas J. Borrelli Senior Lecturer Srinivas Sridharan PhD Student.
Deep learning is a rich branch of machine learning. Our work on deep learning covers foundational theoretical work in the fields of mathematical statistics, logic, learning and algorithms. It also covers a wide range of applications, including object recognition, speech recognition, tracking in HD video, decision making with deep features, imitation learning and reinforcement learning.