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Visar resultat 1 - 5 av 19 uppsatser som matchar ovanstående sökkriterier.
1. Real-Time Continuous Euclidean Distance Fields for Large Indoor Environments
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Real-time spatial awareness is essential in areas such as robotics and autonomous navigation. However, as environments expand and become increasingly complex, maintaining both a low computational load and high mapping accuracy remains a significant challenge. LÄS MER
2. Adapting to Perceived Safety within Human-Drone Interaction : Explored in proximity space
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : During recent years, drones’ presence in society has grown. They have many use cases, such as capturing video footage, delivering packages, protecting farmers’ land against wildlife, and fighting fires. Even though the amount of interactions differ, the drones somehow interact with humans in these use cases. LÄS MER
3. Galaxies as Clocks and the Universal Expansion
Master-uppsats, KTH/FysikSammanfattning : The Hubble parameter H(z) is a measure of the expansion rate of the universe at redshift z. One method to determine it relies on inferring the slope of the redshift with respect to cosmic time, where galaxy ages can be used as a proxy for the latter. This method is used by Simon et al. LÄS MER
4. Constrained Gaussian Process Regression Applied to the Swaption Cube
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : This document is a Master Thesis report in financial mathematics for KTH. This Master thesis is the product of an internship conducted at Nexialog Consulting, in Paris. This document is about the innovative use of Constrained Gaussian process regression in order to build an arbitrage free swaption cube. LÄS MER
5. Machine Unlearning and hyperparameters optimization in Gaussian Process regression
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The establishment of the General Data Protection Regulation (GDPR) in Europe in 2018, including the "Right to be Forgotten" poses important questions about the necessity of efficient data deletion techniques for trained Machine Learning models to completely enforce this right, since retraining from scratch such models whenever a data point must be deleted seems impractical. We tackle such a problem for Gaussian Process Regression and define in this paper an efficient exact unlearning technique for Gaussian Process Regression which completely include the optimization of the hyperparameters of the kernel function. LÄS MER