Essays about: "fully convolutional neural networks"
Showing result 16 - 20 of 74 essays containing the words fully convolutional neural networks.
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16. An evaluation of deep learning models for urban floods forecasting
University essay from KTH/GeoinformatikAbstract : Flood forecasting maps are essential for rapid disaster response and risk management, yet the computational complexity of physically-based simulations hinders their application for efficient high-resolution spatial flood forecasting. To address the problems of high computational cost and long prediction time, this thesis proposes to develop deep learning neural networks based on a flood simulation dataset, and explore their potential use for flood prediction without learning hydrological modelling knowledge from scratch. READ MORE
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17. Artificial Value-at-Risk : Using Neural Networks to Replicate Filtered Historical Simulation for Value-at-Risk Calculations
University essay from Umeå universitet/Institutionen för matematik och matematisk statistikAbstract : Since financial markets are considered risky, there is a need to have credible tools that can estimate these risks. For a Central Clearing Counterparty it is of utmost importance to conduct accurate estimations of its members’ risk exposures to deter-mine their margin requirements. READ MORE
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18. Artificial Intelligence applications for railway signalling
University essay from KTH/TransportplaneringAbstract : The main purpose of this Master Thesis is to investigate how front-facing, train-mounted cameras and Computer Vision, a type of Artificial Intelligence (AI), can be used to compensate for GPS inaccuracies. By using footage from track-recording cameras, Computer Vision can be utilized to determine the number of tracks and the track occupancy of the train, which would compensate GPS inaccuracies in the lateral positioning. READ MORE
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19. The derivation of first- and second-order backpropagation methods for fully-connected and convolutional neural networks
University essay from Lunds universitet/Matematik LTH; Lunds universitet/MatematikcentrumAbstract : We introduce rigorous theory for deriving first and second order backpropagation methods for Deep Neural Networks (DNN) whilst satisfying existing theory in DNN optimization. We begin by formally defining a neural network with its respective components and state the first and second order chain rule with respect to its partial derivatives. READ MORE
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20. Using Mask R-CNN for Instance Segmentation of Eyeglass Lenses
University essay from KTH/Matematisk statistikAbstract : This thesis investigates the performance of Mask R-CNN when utilizing transfer learning on a small dataset. The aim was to instance segment eyeglass lenses as accurately as possible from self-portrait images. Five different models were trained, where the key difference was the types of eyeglasses the models were trained on. READ MORE
