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 18 of 18 for “"AutoML"”.
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Democratizing Deep Learning methods by means of AutoML tools
… imágenes. Para ello se han empleado técnicas de AutoML que buscan de forma automática el mejor modelo para un conjunto de imágenes dadas. Además, hemos desarrollado un método para aplicar aumento de datos a distintos problemas de Visión por Computador. Este método ha sido implementado en una …
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Model Compression and AutoML for Efficient Click-Through Rate Prediction
… Furthermore, we present a new compression-based AutoML method for feature set generation in architectures which incorporate explicit feature interactions. This works as a tool to build efficient recommender system models, and is applicable to many state of the art model designs. Applying this …
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Bayesian tuning and bandits : an extensible, open source library for AutoML
The goal of this thesis is to build an extensible and open source library that handles the problems of tuning the hyperparameters of a machine learning pipeline, selecting between multiple pipelines, and recommending a pipeline. We devise a library that users can integrate into their existing …
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Meta-Feature Taxonomy for Supporting Automatic Machine Learning
Many automatic machine learning (AutoML) libraries have been developed recently, meeting public demand for more machine learning tools which can be used without an expert. A common tactic illicited by these frameworks is to initially generate meta-features which are then used as an initial …
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Mining Software Artifacts for use in Automated Machine Learning
… are commonly termed automated machine learning (AutoML). Inspired by the success of software mining in areas such as code search, program synthesis, and program repair, we investigate the hypothesis that information mined from software artifacts can be used to build, improve interactions with, …
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Automated Machine Learning for Predicting Trends in Time Series Data
… features. A recent Automated Machine Learning (AutoML) namely the hybrid Bayesian optimisation and hyperband (BOHB) framework is implemented and evaluated for ASHO. The AutoML models are then compared to the manually tuned models. The results show that in general TreNet still performs better …
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Efficient Multi-Objective NeuroEvolution in Computer Vision and Applications for Threat Identification
… objectives. Automated machine learning (AutoML) is a flourishing field which endeavours to dis- cover and optimise models and hyperparameters autonomously, providing an alternative to classic, effort-intensive hyperparameter search. However, existing approaches typ- ically show …
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Democratizing data science through interactive curation of ML pipelines
… on over 300 datasets and compare against other AutoML tools, including the current NIPS winner, as well as expert solutions. Not only is Alpine Meadow able to significantly outperform the other AutoML systems while -- in contrast to the other systems -- providing interactive latencies, but also …
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Enhancing gait recognition with 3D markerless motion capture
… motion capture and automated machine learning (AutoML) methods. Advances in 3D markerless motion capture enable full 3D body poses to be estimate from unconstrained video sources. The motion capture algorithm is used to produce 3D body poses from the CASIA-B gait dataset, which in turn are used …
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Artificial intelligence in business analytics, capturing value with machine learning applications in financial services
… Gradient Boosting. Automated Machine Learning (AutoML) was benchmarked against manually tuned models and proved to be a valuable tool to democratize predictive analytics for small to medium-sized corporations and to tackle the skill shortage for ML experts. AutoML has the potential to completely …
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Leveraging lower fidelity proxies for neural network based NAS predictors
… dôležitú časť automatického strojového učenia (AutoML), keďže sa zameriava na automatizá- ciu dovtedajšieho manuálneho hľadania najlepšej architektúry pre danú úlohu. Neod- deliteľnou súčasťou prehľadávania architektúr neurónových sietí je predikcia ich výkon- nosti. Keďže klasické úplné …
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Total Organic Carbon and Clay Estimation in Shale Reservoirs Using Automatic Machine Learning
… built models. Automatic machine learning (AutoML) appears as a promising tool to solve those realistic questions by training multiple models and compares them automatically. Two wells with conventional well loggings and elemental capture spectroscopy are selected from a shale gas play to …
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Orion – A Machine Learning Framework for Unsupervised Time Series Anomaly Detection
… a series of experiments from benchmarking to AutoML on three public time series datasets: NASA, Yahoo, and Numenta. In addition, we showcase the usability of our framework through a study conducted on a real-world use case involving spacecraft experts tasked with anomaly analysis tasks. …
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Gaining Perspective with an Evolutionary Cognitive Architecture for Intelligent Agents
… surrounding the creation of strong AI using AutoML (Automatic Machine Learning) through the development of a general cognitive architecture called Brain Evolver. To do this, the notion of what intelligence is in the context of machines and how it can practically be applied to physical …
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Simplifying the usage and construction of deep image classification models
… Learning, hemos desarrollado una herramienta de AutoML, llamada ATLASS, que asiste al usuario en todo el proceso de creación de un modelo de clasificación de imágenes, desde la anotación de las imágenes, hasta la creación y uso de dicho modelo de Deep Learning. Esta herramienta ha sido validada …
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Enhancing Algorithmic Early Warning Systems with Dynamic Selection to Predict High School Graduation Outcomes
… to be the most accurate, with Random Forest and AutoML approaches being particularly effective. The highest averaged cross-validated prediction accuracy was 97.59% for grade 10, 97.94% for grade 11, and 98.77% for grade 12, achieving actionable certainty in all three grade levels. …
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Design-Space Exploration of Biologically-Inspired SNN Models for Application-Specific Many-Core Systems
… Building upon this groundwork, a standalone AutoML concept in the SNN domain is presented to improve their adaptation of the most suitable model based on the complicated patterns in the specific dataset. The proposed AutoML-SNN algorithm is designed specifically for the utilization of SNN …
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Towards efficient, on-demand and automated deep learning
In the past decade, deep learning has achieved great breakthroughs on tasks of computer vision, speech, language, control and many others. The advanced and dedicated computing chips, like Nvidia GPU and Google TPU, largely contributed and broadened this success. However, the requirement of large …