Sökning: "Multitask Learning"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Multitask Learning.

  1. 1. Energy Consumptions for Vehicles using Multitask Learning

    Master-uppsats, Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Venkata Sai Vivek Uddagiri; Shankara Narayanan Bangalore Ramalingam; [2022]
    Nyckelord :;

    Sammanfattning : This thesis aims to predict energy (fossil fuel and electric) consumption of internal combustion and hybrid vehicles. This thesis is in association with Wireless cars. Accurate prediction of energy consumption in vehicles is vital, as it can pave the way for a more sustainable future. LÄS MER

  2. 2. Reducing Unintended bias in Text Classification using Multitask learning.

    Magister-uppsats, Blekinge Tekniska Högskola; Blekinge Tekniska Högskola

    Författare :Venkata Sai Sukesh Settipalli; Naga Manendra Kumar Dasireddy; [2021]
    Nyckelord :;

    Sammanfattning : .... LÄS MER

  3. 3. Multitask Convolutional Neural Network Emulators for Global Crop Models - Supervised Deep Learning in Large Hypercubes of Non-IID Data

    Master-uppsats, Lunds universitet/Matematisk statistik

    Författare :Amanda Nilsson; [2020]
    Nyckelord :Multitask Learning; Convolutional Neural Network CNN ; Branched Neural Network; Dynamic Global Vegetation Models DGVM ; Automated Feature Extraction; Feature Importance; Supervised Machine Learning; Emulator; Surrogate Model; Response Surface Model; Approximation Model; Metamodeling; Model Composition; Regularization; Robustness; Hyperparameter Optimization; Mathematics and Statistics;

    Sammanfattning : The aim of this thesis is to establish whether a neural network (NN) can be used for emulation of simulated global crop production - retrieved from the computationally demanding dynamic global vegetation model (DGVM) Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS). It has been devoted to elaboration with various types of neural network architectures: Branched NNs capable of processing inputs of mixed data types; Convolutional Neural Network (CNN) architectures able to perform automated temporal feature extraction of the given weather time series; simpler fully connected (FC) structures as well as Multitask NNs. LÄS MER

  4. 4. Latent Representation of Tasks for Faster Learning in Reinforcement Learning

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Felix Engström; [2019]
    Nyckelord :;

    Sammanfattning : Reinforcement learning (RL) is an field of machine learning (ML) which attempts to approach learning in an manner inspired by the human way of learning through rewards and penalties. As with other forms of ML, it is strongly dependent on large amounts of data, the acquisition of which can be costly and time consuming. LÄS MER

  5. 5. Multitask Deep Learning models for real-time deployment in embedded systems

    Uppsats för yrkesexamina på avancerad nivå, KTH/Robotik, perception och lärande, RPL

    Författare :Miquel Martí Rabadán; [2017]
    Nyckelord :computer vision; deep learning; multitask learning; object detection; semantic segmentation; embedded systems; perception; robotics; autonomous driving;

    Sammanfattning : Multitask Learning (MTL) was conceived as an approach to improve thegeneralization ability of machine learning models. When applied to neu-ral networks, multitask models take advantage of sharing resources forreducing the total inference time, memory footprint and model size. LÄS MER