Searched for: subject%3A%22Transfer%255C+learning%22
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Witting, Emiel (author)
Domain adaptation allows machine learning models to perform well in a domain that is different from the available train data. This non-trivial task is approached in many ways and often relies on assumptions about the source (train) and target (test) domains. Unsupervised domain adaptation uses unlabeled target data to mitigate a shift or bias...
bachelor thesis 2023
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Mînea, Robert (author)
Privacy in today's world is a very important topic and all the more important when sizeable amounts of data are needed in Neural Network processing models. Federated Learning is a technique which aims to decentralize the training process in order to allow the clients to maintain their privacy, while also contributing to a broader learning...
bachelor thesis 2021
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HADJIGEORGIOU, MARIOS (author)
Federated Learning (FL) is widely favoured in the training of machine learning models due to its privacy-preserving and data diversity benefits. In this research paper, we investigate an extension of FL referred to as Personalized Federated Learning (PFL) for the purpose of training diffusion models. We explore the personalization technique of...
bachelor thesis 2023
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Verschuren, Wim (author)
Non-Intrusive Load Monitoring (NILM) is a technique used to disaggregate household power consumption data into individual appliance components without the need for dedicated meters for each appliance. This paper focuses on improving the generalizability of NILM algorithms to unseen households using Convolutional Neural Networks (CNNs) and one...
bachelor thesis 2023
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van Lil, Wouter (author)
Using transfer learning, convolutional neural networks for different purposes can have similar layers which can be reused by caching them, reducing their load time. Four ways of loading and executing these layers, bulk, linear, DeepEye and partial loading, were analysed under different memory constraints and different amounts of similar networks...
bachelor thesis 2020
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Dondera, Alin (author)
Moral values play a crucial role in our decision-making process by defining what is right and wrong. With the emergence of political activism and moral discourse on social media, and the latest developments in Natural Language Processing, we are looking at an opportunity to analyze moral values to observe trends as they form. Recent studies have...
bachelor thesis 2021
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Gao, Q. (author), Yang, Haoyu (author), Shanbhag, S.M. (author), Schweidtmann, A.M. (author)
Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, reinforcement learning (RL) has shown some success in automating process design by integrating data-driven models that learn to build process flowsheets with process...
book chapter 2023
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Schulze Balhorn, L. (author), Gao, Q. (author), Goldstein, Dominik (author), Schweidtmann, A.M. (author)
Flowsheets are the most important building blocks to define and communicate the structure of chemical processes. Gaining access to large data sets of machine-readable chemical flowsheets could significantly enhance process synthesis through artificial intelligence. A large number of these flowsheets are publicly available in the scientific...
book chapter 2022
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Ponnambalam, C.T. (author), Kamran, Danial (author), Simão, T. D. (author), Oliehoek, F.A. (author), Spaan, M.T.J. (author)
conference paper 2022
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Kim, Jaehun (author), Won, Minz (author), Serra, Xavier (author), Liem, C.C.S. (author)
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist labels are available for a...
conference paper 2018
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Li, Haobo (author), Le Kernec, Julien (author), Mehul, Ajay (author), Fioranelli, F. (author)
This paper discusses a fusion framework with data from multiple, distributed radar sensors based on conventional classifiers, and transfer learning with pre-trained deep networks. The application considered is the classification of gait styles and the detection of critical accidents such as falls. The data were collected from a network comprised...
conference paper 2020
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Żelasko, Piotr (author), Moro-Velázquez, Laureano (author), Hasegawa-Johnson, Mark (author), Scharenborg, O.E. (author), Dehak, Najim (author)
Only a handful of the world’s languages are abundant with the resources that enable practical applications of speech processing technologies. One of the methods to overcome this problem is to use the resources existing in other languages to train a multilingual automatic speech recognition (ASR) model, which, intuitively, should learn some...
conference paper 2020
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Guo, Xuqi (author), Yan, F. (author), Pang, Y. (author), Yan, Gaowei (author)
In the operation process of wet ball mill, there are often multi-modal and multi-condition problems. In this paper, a multi-view based domain adaptive extreme learning machine (MVDAELM) was used to measure the mill load. Firstly, the correlation relationship between the load parameters and the two views (vibration and acoustic signals of the...
conference paper 2019
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Zhang, Xinyu (author), Abbasi, Qammer H. (author), Fioranelli, F. (author), Romain, Olivier (author), Le Kernec, Julien (author)
Population ageing has become a severe problem worldwide. Human activity recognition (HAR) can play an important role to provide the elders with in-time healthcare. With the advantages of environmental insensitivity, contactless sensing and privacy protection, radar has been widely used for human activity detection. The micro-Doppler...
conference paper 2022
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Mourragui, S.M.C. (author)
Extensive efforts in cancer research over the past decades have markedly improved diagnosis and treatments, leading to better outcomes for cancer patients. Paradoxically, however, these discoveries have begun to shed light on a level of complexity that rules out the emergence of a universal cancer treatment. As any tumor is now known to be...
doctoral thesis 2023
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Kim, Jaehun (author)
Machine learning (ML) has become a core technology for many real-world applications. Modern ML models are applied to unprecedentedly complex and difficult challenges, including very large and subjective problems. For instance, applications towards multimedia understanding have been advanced substantially. Here, it is already prevalent that...
doctoral thesis 2021
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Junell, J. (author)
The use of Micro Aerial Vehicles (MAVs) in practical applications, to solve real-world problems, is growing in demand as the technology becomes more widely known and accessible. Proposed applications already span a wide berth of fields like military, search and rescue, ecology, artificial pollinators, and more. As compared to larger Unmanned...
doctoral thesis 2018
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Yang, Yang (author), Yang, Xiaoyi (author), Sakamoto, Takuya (author), Fioranelli, F. (author), Li, Beichen (author), Lang, Yue (author)
In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably...
journal article 2022
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Chang, Z. (author), Wan, Z. (author), Xu, Y. (author), Schlangen, E. (author), Šavija, B. (author)
Extrusion-based 3D concrete printing (3DCP) results in deposited materials with complex microstructures that have high porosity and distinct anisotropy. Due to the material heterogeneity and rapid growth of cracks, fracture analysis in these air-void structures is often complex, resulting in a high computational cost. This study proposes a...
journal article 2022
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Yin, Zhao (author), Geraedts, Victor Jacobus (author), Wang, Z. (author), Contarino, Maria Fiorella (author), Dibeklioglu, H. (author), van Gemert, J.C. (author)
Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the...
journal article 2022
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