Sökning: "Data Sparsity"

Visar resultat 1 - 5 av 44 uppsatser innehållade orden Data Sparsity.

1. 1. ISAR Imaging Enhancement Without High-Resolution Ground Truth

   Master-uppsats, Linköpings universitet/Datorseende

   Författare :Moltas Enåkander; [2023]
   Nyckelord :SAR; SAR Imaging; ISAR; ISAR Imaging; Machine learning; Convolutional neural network; CNN; neural network; Super resolution; Unsupervised learning;

   Sammanfattning : In synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR), an imaging radar emits electromagnetic waves of varying frequencies towards a target and the backscattered waves are collected. By either moving the radar antenna or rotating the target and combining the collected waves, a much longer synthetic aperture can be created. LÄS MER

3. 2. Over-the-Air Federated Learning with Compressed Sensing

   Master-uppsats, Linköpings universitet/Kommunikationssystem

   Författare :Adrian Edin; [2023]
   Nyckelord :machine learning; ML; Federated Learning; FL; Over-the-air; Over-the-air computation; OtA; OtA computation; AirComp; Compressed sensing; CS; Iterative Hard thresholding; IHT;

   Sammanfattning : The rapid progress with machine learning (ML) technology has solved previously unsolved problems, but training these ML models requires ever larger datasets and increasing amounts of computational resources. One potential solution is to enable parallelization of the computations and allow local processing of training data in distributed nodes, such as Federated Learning (FL). LÄS MER

4. 3. Trainable Region of Interest Prediction: Hard Attention Framework for Hardware-Efficient Event-Based Computer Vision Neural Networks on Neuromorphic Processors

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Cina Arjmand; [2023]
   Nyckelord :Artifical Intelligence; Machine Learning; Neuromorphic Engineering; Computer Vision; Technology and Engineering;

   Sammanfattning : Neuromorphic processors are a promising new type of hardware for optimizing neural network computation using biologically-inspired principles. They can effectively leverage information sparsity such as in images from event-based cameras, and are well-adapted to processing event-based data in an energy-efficient fashion. LÄS MER

5. 4. LDPC DropConnect

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

   Författare :Xi Chen; [2023]
   Nyckelord :Bayesian approach; Machine learning; Coding theory; Measurement uncertainty; Algorithms; Bayesiansk metod; Maskininlärning; Kodningsteori; Mätosäkerhet; Algoritmer;

   Sammanfattning : Machine learning is a popular topic that has become a scientific research tool in many fields. Overfitting is a common challenge in machine learning, where the model fits the training data too well and performs poorly on new data. LÄS MER

7. 5. Modelling Risk in Real-Life Multi-Asset Portfolios

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Karin Hahn; Axel Backlund; [2023]
   Nyckelord :Risk modelling; multi-asset portfolios; risk factor models; time series analysis; regression; Riskmodellering; finansiella portföljer; riskfaktormodeller; tidsserieanalys; regression;

   Sammanfattning : We develop a risk factor model based on data from a large number of portfolios spanning multiple asset classes. The risk factors are selected based on economic theory through an analysis of the asset holdings, as well as statistical tests. LÄS MER