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 10 of 10 for “"Transformer networks"”.
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Optical Temperature Monitoring of Electrical Power Transformer Networks
… network model designed to monitor temperature of transformers at multiple substations from one central location. Fiber Bragg Grating (FBG) has been used as the optical sensing element for transformer temperature monitoring. Suitable FBG parameters and specifications have been investigated and …
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Transformer Networks for Smart Cities: Framework and Application to Makassar Smart Garden Alleys
… S&CC problem sets concurrently. Attention-based Transformer networks are of particular interest given their success across diverse fields of natural language processing (NLP), computer vision, time-series regression, and multi-modal data fusion in recent years. This begs the question whether …
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Generative methods for image synthesis with applications to medical imaging
… use of a hybrid model of generative adversarial networks (GANs) and transformer networks for image synthesis. We propose a method that combines GANs with transformer networks to address the translation and super-resolution of medical images. We also present a model for inpainting of images. …
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Semantic segmentation architecture based on Atrous Spatial Pyramid Pooling and convolutional based attention module for mapping of burned areas in satellite images
… successful in natural language processing tasks, transformer networks have recently been adapted for computer vision applications. These networks utilize self-attention layers, which are crucial to their design. SegFormer is a transformer-based model for semantic segmentation that has shown …
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Resource-Efficient Machine Learning via Count-Sketches and Locality-Sensitive Hashing (LSH)
… to handle these datasets. (e.g., large-scale transformer networks for language modeling). The increase in the number of input features, model size, and output classification space is straining our limited computational resources. Given vast amounts of data and limited computational resources, …
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Learning deep patient representations for the teleICU
… representations of teleICU clinical data using Transformer networks, inspired by recent machine learning literature in language modeling. The utility of these representations is evaluated in various prediction outcome tasks, in which they were able to outperform linear and neural baselines. Also …
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Study of Radiation Effects on FPGA and GPU based Neural Networks Accelerator Designs
… very valuable research. Convolutional neural networks (CNNs), as the most widely used model for deep learning, performs well in image recognition and target detection. Field Programmable Gate Arrays (FPGAs), with their high degree of parallelism, programmability, and low power consumption, are …
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Random Features for Efficient Attention Approximation
Transformers are, perhaps, the most widespread architectures in today's landscape of deep learning. They, however, do not scale well with long sequences, resulting in O(L^2) computational complexity for the sequence length L. In this thesis, we propose a holistic approach for reducing this …
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Machine Learning Force Fields for Modelling Reactions at Complex Interfaces
… interactions called matrix function neural networks (MFNs). By mimicking the ground truth quantum mechanical methods, MFNs can model highly non-local systems. To date, no other architectures can capture the non-locality of cumulene chains and extrapolate to unseen chain lengths, including …
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Accelerating graph computation with system optimizations and algorithmic design
… centrality and a novel formulation of the graph transformer network as a graph algorithm that allows for more efficient computation. 1. Min Rounds Betweenness Centrality (MRBC) is a provably round efficient BC algorithm that uses a novel message update rule in order to only send out updates from …