Searched for: subject%3A%22unsupervised%255C+learning%22
(1 - 18 of 18)
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Liu, Kevin (author)
This master’s thesis explores the application of Self-Supervised Contrastive Learning (SSCL), specifically the SimCLR algorithm, to enhance feature representation learning from Wafer Bin Maps (WBM) in the semiconductor manufacturing process. The motivation stems from the industry’s growing need for automated defect detection and root-cause...
master thesis 2023
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Savu, Ioana (author)
Side-channel attacks (SCA) play a crucial role in assessing the security of the implementation of cryp- tographic algorithms. Still, traditional profiled attacks require a nearly identical reference device to the target, limiting their practicality. This thesis focuses on non-profiled SCA, which provides a re- alistic alternative when the...
master thesis 2023
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Vilhjálmsson, Thor (author)
This thesis aims to investigate the feasibility of developing a successful unsupervised Structural Health Monitoring (SHM) methodology to detect damage in structures, specifically bridges. Detecting damage, especially in its earliest stages, is challenging, thus prompting the need for robust and effective methods. The success of such a...
master thesis 2023
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de Boer, Wouter (author)
Autonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system...
master thesis 2023
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Cheng, Yuxing (author)
To analyze latent multiple specific patterns in the line-based public transport daily delay occurrence, a data-driven explorative analysis of public transport daily delay spatial-temporal distribution pattern is performed based on the k-means clustering algorithm. Firstly, we used aggregated daily delay profile to visualize how the delay is...
student report 2023
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Gold, Andrew (author)
Ranking algorithms in traditional search engines are powered by enormous training data sets that are meticulously engineered and curated by a centralized entity. Decentralized peer-to-peer (p2p) networks such as torrenting applications and Web3 protocols deliberately eschew centralized databases and computational architectures when designing...
master thesis 2023
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Noorthoek, Sterre (author)
In addition to delivering groceries at customers’ doorsteps, online supermarket Picnic goes the extra mile by aiming to improve customer satisfaction. For instance, by providing cooking inspiration to customers through a recently launched recipe page in the app. This feature presents new recipes weekly and allows customers to easily add the...
master thesis 2022
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Dimitrov, Yordan (author)
In this paper we analyze the performance of a novel clustering objective that optimizes a neural network to predict segmentation. We challenge the reported results by replicating the original experiments and conducting additional tests to gain an insight into the algorithm. We analyzed the efficiency of the clustering objective on a different...
master thesis 2021
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Cao, Qingyuan (author)
This project is dedicated to implementing an unsupervised learning clustering method system for processing big data applied in Intel SGX. Intel SGX is a technology developed to meet the needs of the trusted computing industry similarly to ARM TrustZone, but this time for desktop and server platforms. It is a set of safety-related instruction...
master thesis 2021
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Sharma, Shipra (author)
We are living in a world full of data. Data capture the characteristics of any entity around us, like living species, properties of scientific experimentation, etc. Moreover, data provides a basis for further analysis, reasoning, or decision-making. One of the most common applications of data analysis is to group data into a set of clusters to...
master thesis 2021
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Trevnenski, Georgi (author)
Video summarization is a task which many researchers have tried to automate with deep learning methods. One of these methods is the SUM-GAN-AAE algorithm developed by Apostolidis et al. which is an unsupervised machine learning method evaluated in this study. The research aims at testing the algorithm's performance on the Breakfast dataset,...
bachelor thesis 2021
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van der Sar, martijn (author)
Pick and place systems that operate in a warehouse setting have been studied a lot recently due to the high economic value for e-commerce companies. In this thesis, the focus is on the perception pipeline that performs object recognition given a certain input data stream (typically RGB-D images). Impressive results regarding object recognition...
master thesis 2021
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Simion-Constantinescu, Andrei (author)
This thesis presents a novel self-supervised approach of learning visual representations from videos containing human actions. Our approach tackles the complex problem of learning without the need of labeled data by exploring to what extent the ideas successfully used for images can be transferred, adapted and extended to videos for action...
master thesis 2020
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Man, K.W. (author)
As software is produced more and more every year, software also gets exploited more. This exploitation can lead to huge monetary losses and other damages to companies and users. The exploitation can be reduced by automatically detecting the software vulnerabilities that leads to exploitation. Unfortunately, the state-of-the-art methods for this...
master thesis 2020
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Pathak, Chinmay (author)
Anomaly detection is a task of interest in many domains. Typical way of tackling this problem is using an unsupervised way. Recently, deep neural network based density estimators such as Normalizing flows have seen a huge interest. The ability of these models to do the exact latent-variable inference and exact log-likelihood calculation with...
master thesis 2019
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Richa, Eduardo (author)
The detection of unusual behavior plays a crucial role in the prevention of illegal and harmful activities such as smuggling, piracy, arms trading, human trafficking and illegal immigration. Also for military applications, it is useful to detect anomalous behavior to provide an alert for potential threats, especially with the more recent...
master thesis 2018
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Hulsebos, Madelon (author)
master thesis 2018
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Blom, W.B. (author)
The digital environment has an ever increasing amount smart programs. Programs that also get smarter every day. They help us filtering spam e-mail and they adjust to show us personalized advertisements. These smart programs observe people and serve (other) people. A robot can be seen as a program with a body. Make the program smart enough and it...
master thesis 2016
Searched for: subject%3A%22unsupervised%255C+learning%22
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