Searched for: subject%3A%22active%255C%2Blearning%22
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Yang, Yazhou (author), Loog, M. (author)
Though much effort has been spent on designing new active learning algorithms, little attention has been paid to the initialization problem of active learning, i.e., how to find a set of labeled samples which contains at least one instance per category. This work identifies the initialization of active learning as a separate and novel...
journal article 2022
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Hardebolle, Cécile (author), Verma, H. (author), Tormey, Roland (author), Deparis, Simone (author)
Background: Research shows that active pedagogies could play an important role in achieving more equitable outcomes for diverse groups of students in Science, Technology, Engineering, and Mathematics (STEM). Although flipped classes are a popular active methodology, there is a lack of high-quality studies assessing their impact in...
journal article 2022
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Welle Donker, F.M. (author), van Loenen, B. (author), Kessler, Carsten (author), Küppers, Natalie (author), Panek, Mark (author), Mansourian, Ali (author), Zhao, Pengxiang (author), Vancauwenberghe, Glenn (author), Tomić, Hrvoje (author), Kević, Karlo (author)
The new concept of Open Spatial Data Infrastructures (Open SDIs) has emerged from an increased interest in open data initiatives together with national and international directives, such as the EU Open Data Directive (Directive (EU) 2019/1024), and the large investment of European public authorities in developing SDIs for sharing spatial data...
conference paper 2022
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Peschl, M. (author), Zgonnikov, A. (author), Oliehoek, F.A. (author), Cavalcante Siebert, L. (author)
Inferring reward functions from demonstrations and pairwise preferences are auspicious approaches for aligning Reinforcement Learning (RL) agents with human intentions. However, state-of-the art methods typically focus on learning a single reward model, thus rendering it difficult to trade off different reward functions from multiple experts. We...
conference paper 2022
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Kola, I. (author), Isufaj, R. (author), Jonker, C.M. (author)
Personal values represent what people find important in their lives, and are key drivers of human behavior. For this reason, support agents should provide help that is aligned with the personal values of the users. To do this, the support agent not only should know the value preferences of the user, but also how different situations in the...
conference paper 2022
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Peschl, Markus (author)
The field of deep reinforcement learning has seen major successes recently, achieving superhuman performance in discrete games such as Go and the Atari domain, as well as astounding results in continuous robot locomotion tasks. However, the correct specification of human intentions in a reward function is highly challenging, which is why state...
master thesis 2021
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Basting, Mark (author)
Multi-label learning is becoming more and moreimportant as real-world data often contains multi-ple labels. The dataset used for learning such aclassifier is of great importance. Acquiring a cor-rectly labelled dataset is however a difficult task.Active learning is a method which can, given anoisy dataset, identify important instances for...
bachelor thesis 2021
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Rozen, Jonathan (author)
Multi-label classification has gained a lot of attraction in the field of computer vision over the past couple of years. Here, each instance belongs to multiple class labels simultaneously. There are numerous methods for Multi-label classification, however all of them make the assumption that either the training images are completely labelled or...
bachelor thesis 2021
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Catshoek, Tom (author)
Active state machine learning algorithms are a class of algorithms that allow us to infer state machines representing certain systems. These algorithms interact with a system and build a hypothesis of what the state machine describing that system looks like according to the behavior they observed. Once the algorithm arrives at a hypothesis, it...
master thesis 2021
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Rocha, I.B.C.M. (author), Kerfriden, P. (author), van der Meer, F.P. (author)
Concurrent multiscale finite element analysis (FE<sup>2</sup>) is a powerful approach for high-fidelity modeling of materials for which a suitable macroscopic constitutive model is not available. However, the extreme computational effort associated with computing a nested micromodel at every macroscopic integration point makes FE<sup>2</sup>...
journal article 2021
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Mansourian, Ali (author), Zhao, Pengxiang (author), Kessler, Carsten (author), Küppers, Natalie (author), Vancauwenberghe, Glenn (author), Lacroix, Lisa (author), Welle Donker, F.M. (author), van Loenen, B. (author), Tomić, Hrvoje (author), Kević, Karlo (author)
This report presents showcases of active teaching and learning in spatial data infrastructure education in the SPIDER partner universities. It includes detailed descriptions of the practices that have been implemented, as well as the results of the evaluation of the practices from an active teaching learning perspective.
report 2021
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Sayin, Burcu (author), Krivosheev, Evgeny (author), Yang, J. (author), Passerini, Andrea (author), Casati, Fabio (author)
Training data creation is increasingly a key bottleneck for developing machine learning, especially for deep learning systems. Active learning provides a cost-effective means for creating training data by selecting the most informative instances for labeling. Labels in real applications are often collected from crowdsourcing, which engages...
journal article 2021
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Pitz, Natalie (author), Schulze Althoff, Jan (author), Welle Donker, F.M. (author), van Loenen, B. (author), Vancauwenberghe, G. (author), Mansourian, Ali (author), Zhao, Pengxiang (author), Kević, Karlo (author), Tomić, Hrvoje (author)
This report looks at different methods of active teaching and learning and the application of these methods at the partner universities of the SPIDER project. Different methods of on-campus and online teaching are presented and reports on experiences in their application at the partner universities are discussed. In combination with the results...
report 2021
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van Deursen, Max (author)
Many applications employ models to represent real-life environments efficiently. To allow these models to be realistic it is commonly fitted using a dataset containing labeled samples. When obtaining a label for a sample from the environment is expensive, it is key that the dataset contains only those samples that aid in providing a realistic...
master thesis 2020
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Li, Mengze (author)
Active learning has the potential to reduce labeling costs in terms of time and money. In practical use, active learning works as an efficient data labeling strategy. Another point of view to look at active learning is to consider active learning as a learning problem, where the training data is queried by the active learner. Under this...
master thesis 2020
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van Roessel, L. (author)
Active inference is a process theory arising from neuroscience which casts perception, action, planning and learning under one optimisation criterion: minimisation of free energy. Current literature on the implementation of discrete state-space active inference focuses on scalability, the comparison to reinforcement learning and its performance...
master thesis 2020
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Teixeira, Rui (author), Nogal Macho, M. (author), O'Connor, Alan (author), Martinez-Pastor, Beatriz (author)
Reliability assessment with adaptive Kriging has gained notoriety due to the Kriging capability of accurately replacing the performance function while performing as a self-improving function for learning procedures. Recent works on adaptive Kriging pursued to improve the efficiency of the active learning through the application of distinct...
journal article 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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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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Nikte, Shreyas (author)
The active learning approach is a special case of semi-supervised machine learning which is able to interactively query the user to reduce the uncertainty of the machine learning model. The approach is useful to minimize the data labeling cost. The project aims to study and use this method to characterize residential electricity users’ demand...
master thesis 2019
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