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 6 of 6 for “"Semi-supervised model"”.
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Data-driven microphone array shape identification in reverberant environments
… in this manner, but it also details a semi-supervised model to learn array deformation parameters.
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Semi-Supervised Deep Learning Frameworks for Transmission-Scale Load Disaggregation and Behind-the-meter Solar Prediction
… is utilized to segment the dataset. Finally, a semi-supervised model is developed to disaggregate nodal load demand profiles. To evaluate the outcomes without ground truth, cross-zero points and various distance matrices, including Wasserstein, symmetrical KL, and area difference metrics are …
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Deep Learning Based Crop Row Detection
… labeled data, we switched our approach from a semi-supervised model to a fully supervised end-to-end crop row detection model using a Feature Pyramid Network or FPN. Central to the FPN is a pyramid pooling module that extracts features from the input image at multiple resolutions. This results …
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Semi-supervised universal yolov3-spp with GIoU loss for autonomous driving object detection under sunny and foggy weather
… a Generative Adversarial Network (GAN) based model called DehazeGAN. Then, an object detection step using GIoU YOLOv3-SPP is performed on the defogged images. Following the two-stage method, we introduce a supervised universal model that deals with object detection in both sunny and foggy …
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Named entity recognition for Icelandic: comparing and combining different machine learning methods
… is a subtask of Information Extraction. A neural model for NER has already been implemented for Icelandic (NeuroNER), but this is as far as we know, the only previous Machine Learning (ML) model for the task in the Icelandic language. The goal of this project was to develop other ML methods that …
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Overcoming uncertainty for within-network relational machine learning
… on parameter estimation, correct the biases, and model the uncertainty of the missing data to improve predictive performance. In particular, we investigate this issue on a variety of modeling scenarios and prediction problems.^ First, we introduce the <em>Transitive Chung Lu</em> random graph …