Now showing items 1-6 of 6
Two-Stream Convolutional Networks for Dynamic Texture Synthesis
This thesis introduces a two-stream model for dynamic texture synthesis. The model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow ...
Machine Learning and its Application in Automatic Change Detection in Medical Images
Change detection is a fundamental problem in various fields, such as image surveillance, remote sensing, medical imaging, etc. The challenge of change detection in medical images is to detect disease-related changes while ...
Voltage and Frequency Recovery in Power System and MicroGrids Using Artificial Intelligent Algorithms
This thesis developed an advanced assessment tools to recover the power system voltage margin to the acceptable values during the disturbance. First, the effect of disturbance in islanded microgrids are analyzed using power ...
Evolving Network Representation Learning Based on Random Walks
Large-scale network mining and analysis is key to revealing the underlying dynamics of networks, not easily observable before. Lately, there is a fast-growing interest in learning low-dimensional continuous representations ...
SuperLattice documents the creation of a hybrid structure that combines human perception with computational methods of processing information. Through SuperLattice I showcase the malleability of data and highlight the ...
Adaptive Momentum for Neural Network Optimization
In this thesis, we develop a novel and efficient algorithm for optimizing neural networks inspired by a recently proposed geodesic optimization algorithm. Our algorithm, which we call Stochastic Geodesic Optimization (SGeO), ...