Sökning: "LSTM"

Visar resultat 1 - 5 av 26 avhandlingar innehållade ordet LSTM.

1. 1. US Equity REIT Returns and Digitalization

   Författare :Birger Axelsson; Han-Suck Song; Herman Donner; Peter Palm; KTH; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; REITs; quantitative easing; quantitative tightening; deep learning; LSTM; REITs; kvantitativa lättnader; kvantitativ åtstramning; djupinlärning; LSTM; Fastigheter och byggande; Real Estate and Construction Management;

   Sammanfattning : This licentiate thesis is a collection of two essays that utilize time-series econometric methods in real estate finance. The first essay applies econometric modelling on Real Estate Investment Trust (REIT) index returns, focusing on estimating the effect of the quantitative easing (QE) and quantitative tightening (QT) programmes on U.S. LÄS MER

3. 2. Selected Topics in Mathematical Modelling: Machine Learning and Tugs-of-War

   Författare :Carmina Fjellström; Kaj Nyström; Andrea Pascucci; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine learning; Neural networks; LSTM; Financial forecasting; Time series analysis; Stochastic gradient descent; Diffusion map; Dimension reduction; Tug-of-war games; Fractional heat operator; Mean value property; Infinity fractional heat operators; Dynamic programming principle; p-Laplacian; Infinity Laplacian; Kolmogorov equation; Stochastic games; Viscosity solutions; Tillämpad matematik och statistik; Applied Mathematics and Statistics;

   Sammanfattning : This thesis concerns selected topics in mathematical modelling, namely in machine learning and stochastic games called tugs-of-war. It consists of four scientific articles. The first and second are about machine learning topics, while the third and fourth articles are about tug-of-war games. LÄS MER

4. 3. Multi-LSTM Acceleration and CNN Fault Tolerance

   Författare :Stefano Ribes; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Compression; SVD; LSTMs; CNNs; Fault Tolerance; Machine Learning; FPGA; Roofline Model; HLS; Caffe;

   Sammanfattning : This thesis addresses the following two problems related to the field of Machine Learning: the acceleration of multiple Long Short Term Memory (LSTM) models on FPGAs and the fault tolerance of compressed Convolutional Neural Networks (CNN). LSTMs represent an effective solution to capture long-term dependencies in sequential data, like sentences in Natural Language Processing applications, video frames in Scene Labeling tasks or temporal series in Time Series Forecasting. LÄS MER

5. 4. Modern developments in insurance: IFRS 17 and LSTM forecasting

   Författare :Lina Palmborg; Filip Lindskog; Mathias Lindholm; Pietro Millossovich; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Mathematical Statistics; matematisk statistik;

   Sammanfattning : The papers presented here cover two different themes, both with applications in life insurance. The focus in the first paper is on determining the financial position and performance of an insurance company, in a accordance with IFRS 17. LÄS MER

7. 5. Supervised and Unsupervised Deep Learning Models for Flood Detection

   Författare :Ritu Yadav; Yifang Ban; Andrea Nascetti; Nicolas Audebert; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Floods; Remote Sensing; Sentinel-1 SAR; Segmentation; Change Detection; DEM; Data Fusion; Time Series; Deep Learning; Unsupervised Learning; Contrastive Learning; Self-Attention; Convolutional LSTM; Variational AutoEncoder VAE ; Geoinformatik; Geoinformatics;

   Sammanfattning : Human civilization has an increasingly powerful influence on the earthsystem. Affected by climate change and land-use change, floods are occurringacross the globe and are expected to increase in the coming years. Currentsituations urge more focus on efficient monitoring of floods and detecting impactedareas. LÄS MER