Searched for: subject%3A%22monotonicity%22
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Gafton, Dinu (author)
Learning curves are useful to determine the amount of data needed for a certain performance. The conventional belief is that increasing the amount of data improves performance. However, recent work challenges this assumption, and shows nonmonotonic behaviors of certain learners on certain problems. This paper presents a new approach for...
bachelor thesis 2024
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Firman Habib, F (author)
Large-diameter monopiles serve as foundations for offshore wind turbines (OWT), and the diameters are now up to 10 meters. These monopiles exhibit lower embedded length-to-diameter (L/D) ratios compared to the conventional monopile that is widely employed in offshore oil and gas platforms. They undergo rotation when subjected to lateral loading...
master thesis 2023
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Sharma, Astha (author)
In the past decade, suction caissons have emerged as a preferred offshore foundation solution for wind turbines due to their silent installation process and potential for recyclability. However, there has been growing speculation regarding the necessity of under base filling, which involves filling the gap between the top plate of the suction...
master thesis 2023
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Socol, Codrin (author)
Learning curves are used to shape the performance of a Machine Learning (ML) model with respect to the size of the set used for training it. It was commonly thought that adding more training samples would increase the model's accuracy (i.e., they are monotone), but recent works show that may not always be the case. In other words, some learners...
bachelor thesis 2023
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Viering, T.J. (author)
This dissertation focuses on safety in machine learning. Our adopted safety notion is related to robustness of learning algorithms. Related to this concept, we touch upon three topics: explainability, active learning and learning curves.<br/><br/>Complex models can often achieve better performance compared to simpler ones. Such larger models are...
doctoral thesis 2023
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van Dobben de Bruyn, J. (author)
doctoral thesis 2023
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Loog, M. (author), Krijthe, J.H. (author), Bicego, Manuele (author)
Arguably, a desirable feature of a learner is that its performance gets better with an increasing amount of training data, at least in expectation. This issue has received renewed attention in recent years and some curious and surprising findings have been reported on. In essence, these results show that more data does actually not...
journal article 2023
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Cheng, Xiaoyang (author), Ibraim, Erdin (author), Liu, Haoyuan (author), Pisano, F. (author), Diambra, Andrea (author)
This paper uses 3D numerical analyses to investigate the stress path experienced by soil elements around large diameter piles in sand subjected to monotonic drained lateral loading. Inspection of the loading-induced stresses in the soil revealed the multiaxial nature of these stress paths, which are characterised by rotation of one or more...
journal article 2023
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Andrade Castanheira, Francisco (author)
The optimization of interplanetary, low-­thrust trajectories is a computationally expensive aspect of preliminary mission design. To reduce the computational burden associated with it, surrogate models can be used as cheap approximations of the original fitness function. Training the surrogate models in a fully online manner can be done to...
master thesis 2022
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Lourenço Baptista, M. (author), Goebel, Kai (author), Henriques, Elsa M.P. (author)
Maintenance decisions in domains such as aeronautics are becoming increasingly dependent on being able to predict the failure of components and systems. When data-driven techniques are used for this prognostic task, they often face headwinds due to their perceived lack of interpretability. To address this issue, this paper examines how...
journal article 2022
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Kisa, Mehmet (author)
Gas extractions are responsible for mostly all induced earthquakes in the Groningen area and this leads to damage of houses and buildings and issues regarding safety. Current strengthening measurements are mostly very visible. However, this can change by using the window frame as part of the structure, particularly interesting for Dutch houses...
master thesis 2021
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Cenedese, Carlo (author), Belgioioso, Giuseppe (author), Grammatico, S. (author), Cao, Ming (author)
In this paper, we present three distributed algorithms to solve a class of Generalized Nash Equilibrium (GNE) seeking problems in strongly monotone games. The first one (SD-GENO) is based on synchronous updates of the agents, while the second and the third (AD-GEED and AD-GENO) represent asynchronous solutions that are robust to communication...
journal article 2021
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Feckan, Michal (author), Marynets, K. (author)
We present original results in study of the second-order differential equation with exponential non-linearities, subjected to the Dirichlet boundary conditions. Using the proper substitution techniques, we reduce the given problem to the study of its lower and upper solutions.
journal article 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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Lukyanov, A. (author), Vuik, Cornelis (author)
Smoothed particle hydrodynamics (SPH) has been extensively used to model high and low Reynolds number flows, free surface flows and collapse of dams, study pore-scale flow and dispersion, elasticity, and thermal problems. In different applications, it is required to have a stable and accurate discretization of the elliptic operator with...
journal article 2020
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de Groot, Arthur (author)
In recent decades, gas production has caused numerous human-induced shallow earthquakes in the province of Groningen, The Netherlands. The buildings in this area were not designed for these unexpected earthquake loads and have shown to be vulnerable. However, current strengthening measures are considered to be time-consuming, expensive, and a...
master thesis 2019
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Sherson, T.W. (author)
Following their conception in the mid twentieth century, the world of computers has evolved from a landscape of isolated entities into a sprawling web of interconnected machines. Yet, given this evolution, many of the methods we use for allowing computers to work together still reflect their inherently isolated origins with the aggregation of...
doctoral thesis 2019
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Musta, E. (author)
In this thesis we address the problem of estimating a curve of interest (which might be a probability density, a failure rate or a regression function) under monotonicity constraints. The main concern is investigating large sample distributional properties of smooth isotonic estimators, which have a faster rate of convergence and a nicer...
doctoral thesis 2019
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Sherson, T.W. (author), Heusdens, R. (author), Kleijn, W.B. (author)
In this paper, we present a novel derivation of an existing algorithm for distributed optimization termed the primal-dual method of multipliers (PDMM). In contrast to its initial derivation, monotone operator theory is used to connect PDMM with other first-order methods such as Douglas-Rachford splitting and the alternating direction method...
journal article 2019
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Lopuhaä, H.P. (author), Musta, E. (author)
We investigate the asymptotic behavior of the Lp-distance between<br/>a monotone function on a compact interval and a smooth estimator<br/>of this function. Our main result is a central limit theorem for the Lp-error<br/>of smooth isotonic estimators obtained by smoothing a Grenander-type<br/>estimator or isotonizing the ordinary kernel...
journal article 2019
Searched for: subject%3A%22monotonicity%22
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