Searched for: subject%3A%22Semi%255C-supervised%255C+Learning%22
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Moradi, M. (author), Gul, F.C. (author), Zarouchas, D. (author)
Developing comprehensive health indicators (HIs) for composite structures encompassing various damage types is challenging due to the stochastic nature of damage accumulation and uncertain events (like impact) during operation. This complexity is amplified when striving for HIs independent of historical data. This paper introduces an AI...
journal article 2024
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Beţianu, Miruna (author)
Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering domain shifts, e.g., classifier of movie reviews from IMDb to Rotten Tomatoes, adapting such an LLM-based multi-label classifier is challenging due to incomplete...
master thesis 2023
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Moradi, M. (author), Chiachío, Juan (author), Zarouchas, D. (author)
Composite structures are highly valued for their strength-to-weight ratio, durability, and versatility, making them ideal for a variety of applications, including aerospace, automotive, and infrastructure. However, potential damage scenarios like impact, fatigue, and corrosion can lead to premature failure and pose a threat to safety. This...
conference paper 2023
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Moradi, M. (author), Gul, F.C. (author), Chiachío, Juan (author), Benedictus, R. (author), Zarouchas, D. (author)
A health indicator (HI) serves as an intermediary link between structural health monitoring (SHM) data and prognostic models, and an efficient HI should meet prognostic criteria, i.e., monotonicity, trendability, and prognosability. However, designing a proper HI for composite structures is a challenging task due to the complex damage...
conference paper 2023
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Dong, Y. (author), Chen, Kejia (author), Ma, Zhiyuan (author)
Condition-based maintenance is becoming increasingly important in hydraulic systems. However, anomaly detection for these systems remains challenging, especially since that anomalous data is scarce and labeling such data is tedious and even dangerous. Therefore, it is advisable to make use of unsupervised or semi-supervised methods, especially...
conference paper 2023
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Wang, Wenhui (author)
Data drift refers to the variation in the production data compare to the training data and sometimes the machine learning model would decay because of it. Some machine learning models face the problem when in production: they receive drift data while there’s no ground truth to evaluate model performance, thus no way of determining the...
master thesis 2022
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Habib, Benjamin (author)
Whereas in the past, Distribution Systems played a passive role in connecting customers to electricity, Distribution System Operators (DSOs) will have to take in the future a more active role in monitoring and regulating the network to deal with the new behaviors and dynamics of the system brought by the energy transition. State Estimation, a...
master thesis 2022
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Patel, T.B. (author), Scharenborg, O.E. (author)
In the diverse and multilingual land of India, Hindi is spoken as a first language by a majority of its population. Efforts are made to obtain data in terms of audio, transcriptions, dictionary, etc. to develop speech-technology applications in Hindi. Similarly, the Gram-Vaani ASR Challenge 2022 provides spontaneous telephone speech, with...
journal article 2022
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Slooff, Tom (author)
One of the most potent attacks against cryptographic implementations nowadays is side-channel attacks. Side-channel attacks use unintended leakages in the implementation, for example, electromagnetic radiation, to retrieve the secret key. Over time side-channel attacks have become more powerful, and recently the community has shifted towards...
master thesis 2021
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Pastor Serrano, O. (author), Lathouwers, D. (author), Perko, Z. (author)
Background and objective: One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical...
journal article 2021
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Lin, Y. (author), Pintea, S. (author), van Gemert, J.C. (author)
Current work on lane detection relies on large manually annotated datasets. We reduce the dependency on annotations by leveraging massive cheaply available unlabelled data. We propose a novel loss function exploiting geometric knowledge of lanes in Hough space, where a lane can be identified as a local maximum. By splitting lanes into separate...
conference paper 2021
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Morette, N. (author), Castro Heredia, L.C. (author), Ditchi, Thierry (author), Mor, A. R. (author), Oussar, Y. (author)
This paper tackles the problem of the classification of partial discharge (PD) and noise signals by applying unsupervised and semi-supervised learning methods. The first step in the proposed methodology is to prepare a set of classification features from the statistical moments of the distribution of the Wavelet detail coefficients extracted...
journal article 2020
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Smalbil, Jos (author)
In order to provide accurate statistics for industries, the classification of enterprises by economic activity is an important task for national statistical institutes. The economic activity codes in the Dutch business register are less accurate for small enterprises since small enterprises are not labelled manually. To increase the quality of...
master thesis 2020
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Mey, A. (author)
The goal of this thesis is to investigate theoretical results in the field of semi-supervised learning, while also linking them to problems in related subjects as class probability estimation.<br/>
doctoral thesis 2020
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Das, Bishwadeep (author), Isufi, E. (author), Leus, G.J.T. (author)
Diffusion-based semi-supervised learning on graphs consists of diffusing labeled information of a few nodes to infer the labels on the remaining ones. The performance of these methods heavily relies on the initial labeled set, which is either generated randomly or using heuristics. The first sometimes leads to unsatisfactory results because...
conference paper 2020
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Mey, A. (author), Viering, T.J. (author), Loog, M. (author)
Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in...
conference paper 2020
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Das, Bishwadeep (author)
In statistical learning over large data-sets, labeling all points is expensive and time-consuming. Semi-supervised classification allows learning with very few labels. Naturally, selecting a few points to label becomes crucial as the performance relies heavily on the labeled points. The motivation behind active learning is to build an optimal...
master thesis 2019
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Jurasiński, Karol (author)
Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled data, which allowed the development of SSL models with the usage of deep neural networks. However, some of these models rely on ad-hoc loss additions...
master thesis 2019
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Bertazzi, Andrea (author)
Semi-supervised algorithms have been shown to possibly have a worse performance than the corresponding supervised model. This may be due to a violation of the assumptions on the data that are introduced in most classification systems. We study an approach that was previously shown to have guarantees of improvement for the LDA classifier in terms...
master thesis 2018
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Mandersloot, Jeroen (author)
Rare category detection is the task of discovering rare classes in unlabelled and imbalanced datasets. Existing algorithms focus almost exclusively on static data in which instances are assumed to be independent. In this thesis we propose an algorithm that is designed for temporal data. Specifically, we are interested in data with temporal...
master thesis 2018
Searched for: subject%3A%22Semi%255C-supervised%255C+Learning%22
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