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Négyesi, B. (author), Andersson, Kristoffer (author), Oosterlee, Cornelis W. (author)A novel discretization is presented for decoupled forward–backward stochastic differential equations (FBSDE) with differentiable coefficients, simultaneously solving the BSDE and its Malliavin sensitivity problem. The control process is estimated by the corresponding linear BSDE driving the trajectories of the Malliavin derivatives of the...journal article 2024
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Vastl, Martin (author), Kulhanek, Jonas (author), Kubalik, Jiri (author), Derner, Erik (author), Babuska, R. (author)Many real-world systems can be naturally described by mathematical formulas. The task of automatically constructing formulas to fit observed data is called symbolic regression. Evolutionary methods such as genetic programming have been commonly used to solve symbolic regression tasks, but they have significant drawbacks, such as high...journal article 2024
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Diware, S.S. (author), Chilakala, Koteswararao (author), Joshi, Rajiv V. (author), Hamdioui, S. (author), Bishnoi, R.K. (author)Diabetic retinopathy (DR) is a leading cause of permanent vision loss worldwide. It refers to irreversible retinal damage caused due to elevated glucose levels and blood pressure. Regular screening for DR can facilitate its early detection and timely treatment. Neural network-based DR classifiers can be leveraged to achieve such screening in...journal article 2024
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Makrodimitris, S. (author), Pronk, I.B. (author), Abdelaal, T.R.M. (author), Reinders, M.J.T. (author)Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the...review 2024
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Versteeg, Rogier (author), Pool, D.M. (author), Mulder, Max (author)This article discusses a long short-term memory (LSTM) recurrent neural network that uses raw time-domain data obtained in compensatory tracking tasks as input features for classifying (the adaptation of) human manual control with single- and double-integrator controlled element dynamics. Data from two different experiments were used to train...journal article 2024
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Voskamp, Rodney (author)PDEs, like HJB-equations, can be solved using grid-based methods. These methods are inefficient for solving high-dimensional HJB-equation, because they suffer from the Curse of Dimensionality. Neural networks may overcome this problem. In this research, we solve high dimensional Partial Integro Differential Equations (PIDE) using neural networks...master thesis 2023
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Köylü, T.C. (author)Machine learning has gained a lot of recognition recently and is now being used in many important applications. However, this recognition was limited in the hardware security area. Especially, very few approaches depend on this powerful tool to detect attacks during operation. This thesis reduces this gap in the field of fault injection attack...doctoral thesis 2023
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Grzejdziak, Michał (author)Neural networks are commonly initialized to keep the theoretical variance of the hidden pre-activations constant, in order to avoid the vanishing and exploding gradient problem. Though this condition is necessary to train very deep networks, numerous analyses showed that it is not sufficient. We explain this fact by analyzing the behavior of the...master thesis 2023
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Ha, Xuan Thao (author), Wu, D. (author), Ourak, Mouloud (author), Borghesan, Gianni (author), Dankelman, J. (author), Menciassi, Arianna (author), Poorten, Emmanuel Vander (author)In this article, a deep learning method for the shape sensing of continuum robots based on multicore fiber bragg grating (FBG) fiber is introduced. The proposed method, based on an artificial neural network (ANN), differs from traditional approaches, where accurate shape reconstruction requires a tedious characterization of many...journal article 2023
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Kubalik, Jiri (author), Derner, Erik (author), Babuska, R. (author)Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior. Symbolic regression is a method that can automatically generate such models from data. Historically, symbolic regression has been predominantly realized by genetic programming, a method that...journal article 2023
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Grande, Davide (author), Peruffo, A. (author), Anderlini, Enrico (author), Salavasidis, Georgios (author)Machine learning-based methodologies have recently been adapted to solve control problems. The Neural Lyapunov Control (NLC) method is one such example. This approach combines Artificial Neural Networks (ANNs) with Satisfiability Modulo Theories (SMT) solvers to synthesise stabilising control laws and to prove their formal correctness. The...journal article 2023
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Diware, S.S. (author), Singh, A. (author), Gebregiorgis, A.B. (author), Joshi, Rajiv V. (author), Hamdioui, S. (author), Bishnoi, R.K. (author)Computation-in-memory (CIM) paradigm leverages emerging memory technologies such as resistive random access memories (RRAMs) to process the data within the memory itself. This alleviates the memory-processor bottleneck resulting in much higher hardware efficiency compared to von-Neumann architecture-based conventional hardware. Hence, CIM...journal article 2023
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Diware, S.S. (author), Dash, Sudeshna (author), Gebregiorgis, A.B. (author), Joshi, Rajiv V. (author), Strydis, C. (author), Hamdioui, S. (author), Bishnoi, R.K. (author)Timely detection of cardiac arrhythmia characterized by abnormal heartbeats can help in the early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices typically use neural networks to provide the most convenient way of continuously monitoring heart activity for arrhythmia detection. However, it is challenging to...journal article 2023
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Focante, E. (author), Martin, Lilian De (author), Coutino, Mario (author)In recent years, convolutional neural networks (CNNs) have been increasingly used for classifying radar micro-Doppler signatures of various targets. However, obtaining large amounts of data for efficient CNN training in defence and surveillance scenarios can be challenging. Therefore, designing techniques that maximize the use of available...conference paper 2023
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Roldan Montero, I. (author), Fioranelli, F. (author), Yarovoy, Alexander (author)A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network...journal article 2023
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Arbabi, Saeed (author), Foppen, Wouter (author), Gielis, Willem Paul (author), van Stralen, Marijn (author), Jansen, Mylène (author), Arbabi, Vahid (author), de Jong, Pim A. (author), Weinans, Harrie (author), Seevinck, Peter (author)Magnetic resonance Imaging is the gold standard for assessment of soft tissues; however, X-ray-based techniques are required for evaluating bone-related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI-based bone visualization technique, compared with CT, for the scoring of knee...journal article 2023
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Allen, G.M. (author), Hu, Andrea (author), Gadiraju, Ujwal (author)Crowdsourcing is a valuable tool to gather human input which enables the development of reliable artificial intelligence systems. Microtask platforms like Prolific and Amazon's Mechanical Turk have flourished by creating environments where crowd workers can provide such human input in a diverse and representative manner. Such marketplaces...conference paper 2023
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Everse, Luc (author)Neural networks (NNs) have, in recent years, become a major part of modern pattern recognition, and both theoretical and applied research evolve at an astounding pace. NNs are usually trained via gradient descent (GD), but research has shown that GD is not always capable of training very small networks. As a result, networks trained via GD are...master thesis 2022
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Nanda, S. (author)Aerosols are the source of the largest uncertainties in our climate models, blurring our outlook of the future. This has been attributed to the complexity of measuring their properties, which vary over time and space. Atmospheric circulation spreads aerosols across the globe from a point source, which makes satellite-based observations lucrative...doctoral thesis 2022
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Van Mieghem, Laurens (author)With the emergence of more complex option pricing models, the demand for fast and accurate numerical pricing techniques is increasing. Due to a growing amount of accessible computational power, neural networks have become a feasible numerical method for approximating solutions to these pricing models. This work concentrates on analysing various...master thesis 2022