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 11 of 11 for “"Machine Learning Training"”.
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Network Requirements for Distributed Machine Learning Training in the Cloud
… the impact of network bandwidth on distributed machine learning training. I test four popular machine learning models (ResNet, DenseNet, VGG, and BERT) on an Nvidia A-100 cluster to determine the impact of bursty and non-bursty cross traffic (such as web-search traffic and long-lived flows) on …
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TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Machine Learning Training Jobs
… a novel approach for building direct-connect DNN training clusters. The proposed system, called TopoOpt, co-optimizes the distributed training process across three dimensions: computation, communication, and network topology. TopoOpt uses a novel alternating optimization technique and a group …
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Towards Efficient AI for Science in Scalable and High Performance Distributed System
… natural language processing. In recent years, machine learning methods have been increasingly applied to the scientific discovery process, accelerating advances in diverse fields. Notable examples include AlphaFold, which predicts protein structures, and ClimateX, which enhances weather …
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A machine learning framework for predictive maintenance of wind turbines
… energy companies have increasingly turned to machine learning to improve wind turbine reliability. Thus, the goal of this thesis is to create a flexible and extensible machine learning framework that enables wind energy experts to define and build models for the predictive maintenance of wind …
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Correct Programs, Executed Correctly: Verifying Specifications And Executions
… correct execution of optimization problems like machine learning training; and Zippel, a language for implementing and automatically verifying properties of non-interactive zero-knowledge protocols. Each one of those works shows that, by carefully designing languages and proof systems for …
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Game of threads: Enabling asynchronous poisoning attacks
… sizes continue to grow at an unprecedented rate, machine learning training is being forced to adopt asynchronous training algorithms to maintain performance and scalability. In asynchronous training, many threads share and update the model in a racy fashion to avoid inter-thread synchronization. …
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The Flexible Face: Unifying the Protocols of Facial Recognition Technologies
… overtly present in the facial databases used in machine learning training and operationalizing of the technology, act under predictive logics to categorize and hierarchize the faces under observation into stable data defined by difference; political protocols, in combinations of state and …
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Influence of training dataset selection on the performance of a machine learning model
… The model has been developed using Deep Learning (DL) based Multi-column Convolutional Neural Network (MCNN) algorithm and TensorFlow framework. This is an object counting model, that counts the Canola flowers from the images based on the learning from a given set of training images, …
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A drilling forensic framework and algorithm for the analysis and diagnoses of drilling dysfunction.
… site are still lacking. Over the past decade, machine learning algorithms have been developed to enhance drilling performance, yet there are limited applications of artificial intelligence for real-time drilling dysfunction identification. This thesis aims to improve drilling performance by …
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Understanding modern deep learning techniques for audio applications and beyond
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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Application of data-driven technologies for asthma self-management
… high levels of active engagement on their part. Machine learning and mobile health (mHealth) have the potential to enable a new form of asthma self-management where tedious daily monitoring is minimised, through a combination of passive monitoring and attack prediction. AIMS AND OBJECTIVES: The …