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Bevelander, Aron (author)Compressed sensing is a framework in signal processing that enables the efficient acquisition and reconstruction of sparse signals. A widelyused class of algorithms that are used for this reconstruction, called greedyalgorithms, depend on nonconvex optimization. With increasing signal size, these problems become computationally very hard....master thesis 2024
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Smeenk, Rutger (author)This research aims at quantifying the uncertainty in the predictions of tensor network constrained kernel machines, focusing on the Canonical Polyadic Decomposition (CPD) constrained kernel machine. Constraining the parameters in the kernel machine optimization problem to be a CPD results in a linear computational complexity in the number of...master thesis 2023
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DenariĆ©, PierreAntoine (author)Blind Source Separation (BSS), the separation of latent source components from observed mixtures, is relevant to many fields of expertise such as neuroimaging, economics and machine learning. Reliable estimates of the sources can be obtained through diagonalization of the cumulant tensor, i.e., a fourthorder symmetric multilinear array...master thesis 2022
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Jonnalagadda, Aravind (author)Natural Language Processing (NLP) deals with understanding and processing human text by any computer software. There are several network architectures in the fields of deep learning and artificial intelligence that are used for NLP. Deep learning techniques like recurrent neural networks and feedforward neural networks are used to develop...master thesis 2022
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Chandrashekar, Rohan (author)Humans make decisions when presented with choices based on influences. The Internet today presents people with abundant choices to choose from. Recommending choices with an emphasis on people's preferences has become increasingly sought. Grundy (1979), the first computer librarian Recommender System (RS), provided users with book recommendations...master thesis 2022
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van Mourik, Ewoud (author)This thesis studies the Canonical Polyadic Decomposition (CPD) constrained kernel machine for large scale learning, i.e. learning with a large number of samples. The kernel machine optimization problem is solved in the primal space, such that the complexity of the problem scales linearly in the number of samples as opposed to scaling cubically...master thesis 2022
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LI, Zhehan (author)This thesis applies the GaussNewton optimizer to estimate the parameter values of the VolterraPARAFAC model by minimizing a nonlinear least square cost (NLS) function given the input and output measurements of the MISO Volterra system.master thesis 2022
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Improving the Computational Speed of a TensorNetworked Kalman Filter for Streaming Video Completionvan Koppen, Bram (author)Streaming video completion is the practice that aims to fill in missing or corrupted pixels in a video stream by using past uncorrupted data. A method to tackle this problem is recently introduced called a Tensor Networked Kalman Filter (TNKF). It shows promising results in terms of performance compared to stateoftheart methods for high...master thesis 2022
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van Klaveren, Pieter (author)Online video completion aims to complete corrupted frames of a video in an online fashion. Consider a surveillance camera that suddenly outputs corrupted data, where up to 95% of the pixels per frame are corrupted. Real time video completion and correction is often desirable in such scenarios. Therefore, this thesis improves the TensorNetworked...master thesis 2021
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WANG, CHENXU (author)Least Squares Support Vector Machines (LSSVMs) are stateoftheart learning algorithms that have been widely used for pattern recognition. The solution for an LSSVM is found by solving a system of linear equations, which involves the computational complexity of O(N^3). When datasets get larger, solving LSSVM problems with standard methods...master thesis 2021
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de Pont, Mike (author)The human sensorimotor system can be seen as a complex network in which the brain plays an important role, resulting in a difficulttounderstand relation between proprioceptive stimuli and cortical responses. However, understanding this relationship is of added value for understanding various diseases which cause dysfunctionality. In recent...master thesis 2020
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Ling, Chenyang (author)Nowadays, video surveillance and motion detection system are widely used in various environments. With the relatively lowprice cameras and highly automated monitoring system, video and image analysis on road, highway and skies becomes realistic. The key process in the analysis is to separate the useful information such as moving foreground...master thesis 2020
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Lucassen, Max (author)Leastsquares supportvectormachines are a frequently used supervised learning method for nonlinear regression and classification. The method can be implemented by solving either its primal problem or dual problem. In the dual problem a linear system needs to be solved, yet for largescale problems this can be impractical as current methods...master thesis 2020
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 Mousa, Amne (author) master thesis 2020
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de Rooij, S.J.S. (author)In streaming video completion one aims to fill in missing pixels in streaming video data. This is a problem that naturally arises in the context of surveillance videos. Since these are streaming videos, they must be completed online and in realtime. This makes the streaming video completion problem significantly more difficult than the related...master thesis 2020
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Tripathy, Navdeep (author)In the thesis, the challenge of precisely developing a datadriven Linear Time Invariant MIMO Reticle Heating Induced Deformation Prediction (RHIDP) model for ASML's DUV systems is presented. The model is developed for two inputs, namely airflow temperature and dose for full field exposures. A reduced order datadriven based approach for...master thesis 2018