Essays about: "Multi-Scale Representation Learning"

Found 2 essays containing the words Multi-Scale Representation Learning.

  1. 1. Multi-Scale Task Dynamics in Transfer and Multi-Task Learning : Towards Efficient Perception for Autonomous Driving

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Simon Ekman von Huth; [2023]
    Keywords : Autonomous Driving; Computer Vision; Deep Learning; Machine Learning; Multi-Task Learning; Transfer Learning; Task Relationships; Task Dynamics; Python; Multi-Scale Representation Learning; Fuss-Free Network; Självkörande Fordon; Datorseende; Djupinlärning; Maskininlärning; Multiuppgiftsinlärning; Överföringsinlärning; Uppgiftsrelationer; Uppgiftsdynamik; Python; Flerskalig Representationsinlärning; Fuss-Free Nätverk;

    Abstract : Autonomous driving technology has the potential to revolutionize the way we think about transportation and its impact on society. Perceiving the environment is a key aspect of autonomous driving, which involves multiple computer vision tasks. READ MORE

  3. 2. Multi-Scale Topology Optimization of Lattice Structures Using Machine Learning

    University essay from Uppsala universitet/Tillämpad mekanik

    Author : Julia Ibstedt; [2023]
    Keywords : Topology optimization; Multi-scale topology optimization; Machine learning; Gaussian process; Homogenization; Inverse homogenization; Anisotropic materials; Symmetry groups; Material property space; Topologioptimering; Flerskalig topologioptimering; Maskininlärning; Gaussian process; Homogenisering; Anisotropa material; Symmetrigrupper; Materialegenskapsrymd;

    Abstract : This thesis explores using multi-scale topology optimization (TO) by utilizing inverse homogenization to automate the adjustment of each unit-cell's geometry and placement in a lattice structure within a pressure vessel (the design domain) to achieve desired structural properties. The aim is to find the optimal material distribution within the design domain as well as desired material properties at each discretized element and use machine learning (ML) to map microstructures with corresponding prescribed effective properties. READ MORE