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 20 of 1747 for “"Supervised"”.
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Supervised manifold distance segmentation
In this paper, I will propose a simple and robust method for image and volume data segmentation based on manifold distance metrics. In this approach, pixels in an image are not considered as points with color values arranged in a grid. In this way, a new data set is built by a transform function …
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Federated self-supervised learning
… current FL practices predominantly focus on supervised learning tasks, necessitating the availability of high-quality, domain-specific labels alongside the data. This prerequisite constrains the implementation of FL in numerous real-world applications where access to such labels at the edge …
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Distributed Supervised Statistical Learning
… In this thesis, we focus on distributed supervised statistical learning where sparse linear regression analysis is performed in a distributed framework. These methods are frequently applied in a variety of disciplines tackling large scale datasets analysis, including engineering, …
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Weakly-supervised text classification
… many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this work, we propose a weakly-supervised framework that addresses the lack of …
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Supervised and self-supervised deep learning approaches for weed identification and soybean yield prediction
… emphasizing the utilization of advanced supervised and self-supervised deep learning approaches for an innovative solution to weed detection and crop yield prediction. The study focuses on key weed species: Italian ryegrass in wheat, Palmer amaranth, and common ragweed in soybean, which …
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GOES-R Supervised Machine Learning
<p>The GOES-R series is a product line of four satellite, with two currently on-orbit (GOES-16 “East” and GOES-17 “West”). GOES-17 is susceptible to a Loop-Heat-Pipe (LHP) phenomenon where during Fall and Spring seasons, there are times of day where some of the infrared bands records inaccurate …
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Self-Supervised Learning for Geometry
… deep learning problems. Differ from conventional supervised learning methods that using expensive annotations as the supervisory signal, we advocate for the use of geometry as a supervisory signal to improve the perceptual capabilities in robots, namely Geometry Self-supervision. With the geometry …
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Improving a supervised CCG parser
The central topic of this thesis is the task of syntactic parsing with Combinatory Categorial Grammar (CCG). We focus on pipeline approaches that have allowed researchers to develop efficient and accurate parsers trained on articles taken from the Wall Street Journal (WSJ). We present three …
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A Study of Supervised Home Experiences
No abstract prepared.
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Coupled similarity analysis in supervised learning
In supervised learning, the distance or similarity measure is widely used in a lot of classification algorithms. When calculating the categorical data similarity, the strategy used by the traditional classifiers often overlooks the inter-relationship between different data attributes and assumes …
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Clustering Via Supervised Support Vector Machines
An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM …
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Structural Self-Supervised Objectives for Transformers
In this Thesis, we leverage unsupervised raw data to develop more efficient pre-training objectives and self-supervised tasks that align well with downstream applications. In the first part, we present three alternative objectives to BERT’s Masked Language Modeling (MLM), namely Random Token …
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Self-supervised multi-contrast MRI denoising
… the "Corruption2Self" (C2S) framework, a self-supervised method for multi-contrast MRI denoising. C2S utilizes self-generated pseudo-labels from noisy data to enhance contrast fusion and Signal-to-Noise Ratio (SNR), providing a robust solution that facilitates shorter scanning times or improved …
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Towards open world semi supervised detection
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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Semi-supervised learning for natural language
Statistical supervised learning techniques have been successful for many natural language processing tasks, but they require labeled datasets, which can be expensive to obtain. On the other hand, unlabeled data (raw text) is often available "for free" in large quantities. Unlabeled data has shown …
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Contrasting contrastive and supervised models interpretability
… thesis, we compare the representations of an unsupervised contrastive model to those of an equivalent supervised model using several deep neural network interpretability methods: network dissection, sparsity experiments, and saliency maps. Network dissections of self-supervised contrastive and …
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Self-Supervised Learning for Speech Processing
Deep neural networks trained with supervised learning algorithms on large amounts of labeled speech data have achieved remarkable performance on various spoken language processing applications, often being the state of the arts on the corresponding leaderboards. However, the fact that training …
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On Semi-supervised Estimation of Distributions
We study the problem of estimating the joint probability mass function (pmf) over two random variables. In particular, the estimation is based on the observation of 𝑚 samples containing both variables and 𝑛 samples missing one fixed variable. We adopt the minimax framework with [notation] loss …
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Supervised Inference of Gene Regulatory Networks
… data. Most of these methods rely on unsupervised or association based strategies, which cannot leverage known regulatory interactions by design. To facilitate supervised learning, we propose a novel graph convolutional neural network (GCN) based autoencoder to infer new regulatory edges …
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