Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 5 of 5 for “"Deep Artificial Neural Networks"”.
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Volatility Model Pricing and Calibration with Neural Networks using Bayesian Optimisation
… Therefore, this dissertation explores the use of deep artificial neural networks to calibrate volatility models by deploying computational resources to train a model (offline) and then utilise the pre-trained model to competitively price options (online). Deep neural networks in this paper are …
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Topographic deep artificial neural network as a model of primate ventral visual stream
… network. While recent work has demonstrated that deep artificial neural net-works (ANNs) optimized for object categorization are strong predictors of neuronal responses at corresponding levels of the primate ventral visual stream (V1, V2, V4, and IT), those models do not explain the spatial …
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Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques
… medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis with artificial intelligence algorithms. This novel approach …
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A Real-Time and Automatic Ultrasound-Enhanced Multimodal Second Language Training System: A Deep Learning Approach
… videos. Machine learning is a subset of Artificial Intelligence (AI), where machines can learn by experiencing and acquiring skills without human involvement. Inspired by the functionality of the human brain, deep artificial neural networks learn from large amounts of data to perform a …
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Modeling Cognition by Pruning and Topography-Learning in Deep Neural Networks
Deep artificial neural networks (DNNs) now match or exceed human accuracy on many benchmarks, yet high performance alone does not imply human-like representational structure. This motivates developing algorithms that not only improve benchmarks but also produce representations that are better …