Searched for: subject%3A%22Active%255C+Learning%22
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Liscio, E. (author)
Human values are the abstract motivations that drive our opinions and actions. AI agents ought to align their behavior with our value preferences (the relative importance we ascribe to different values) to co-exist with us in our society. However, value preferences differ across individuals and are dependent on context. To reflect diversity in...
doctoral thesis 2024
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Meng, Zeng (author), Kong, Lin (author), Jiaxiang, Y. (author), Peng, Hao (author)
This paper proposes a new active learning method named as optimum-pursuing method (OPM) from the viewpoint of optimization theory, which aims to provide an effective tool for solving constrained optimization and reliability-based design optimization (RBDO) problems with low computation cost. It uses the cheap Kriging metamodel to replace the...
journal article 2024
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Vilakathara, Arjun (author)
Accurate segmentation of anatomical structures and abnormalities in medical images is crucial, but manual segmentation is time-consuming and automated approaches lack clinical accuracy. In recent years, active learning approaches that aim to combine automatic segmentation with manual input have gained attention in the field, aiming to reduce the...
bachelor thesis 2023
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van Marrewijk, Jonas (author)
Segmentation of 3D medical images is useful for various medical tasks. However, fully automated segmentation lacks the accuracy required for medical purposes while manual segmentation is too time-consuming. Therefore, an active learning method can be used to generate an accurate segmentation using less user input. The active learning pipeline...
bachelor thesis 2023
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Wei, Wei (author)
Active learning has been proposed as a solution to mitigate the expensive and time-consuming process of annotating large-scale autonomous driving datasets. The process typically involves a model initialization phase, followed by multiple iterations aiming at selecting the most informative data based on the initial model. However, we find two...
master 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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Ren, Chao (author), Tan, J. (author), Xing, Yihan (author)
Wave energy is considered one of the most potential renewable energy. In the last two decades, many wave energy converters (WECs) have been designed to harvest energy from the ocean. Different power take-off systems are developed to maximize the power generation of WECs. However, the estimation of the power matrix of the WECs and annual power...
journal article 2023
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Sayin, Burcu (author), Yang, J. (author), Passerini, Andrea (author), Casati, Fabio (author)
In many practical applications, machine learning models are embedded into a pipeline involving a human actor that decides whether to trust the machine prediction or take a default route (e.g., classify the example herself). Selective classifiers have the option to abstain from making a prediction on an example they do not feel confident about...
conference paper 2023
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Celemin, Carlos (author), Kober, J. (author)
In order to deploy robots that could be adapted by non-expert users, interactive imitation learning (IIL) methods must be flexible regarding the interaction preferences of the teacher and avoid assumptions of perfect teachers (oracles), while considering they make mistakes influenced by diverse human factors. In this work, we propose an IIL...
journal article 2023
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van Loenen, B. (author), Ploeger, H.D. (author), van Everdingen, N.A.L. (author), Cuervo, Kristian (author), Monahan, Jessica (author), Pille, Julia (author), Verhaeghe, Carmel (author)
A new active teaching and learning approach has been implemented in the BSc course Open Urban Data Governance.. This course is part of the minor Spatial Computing for Digital Twinning in the Bachelor of Architecture, Urbanism and Building Sciences at the Faculty of Architecture and The Built Environment, TU Delft, and offered TU wide as an...
conference paper 2023
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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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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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Welle Donker, F.M. (author), van Loenen, B. (author), Poslončec Petrić, Vesna (author)
There is an increasing need for spatial data to be used for informed decision-making and as a resource for developing innovative products and services. A Spatial Data Infrastructure (SDI) facilitates access to and sharing of spatial data by providing a framework in which technical and non-technical aspects are established. Traditionally, SDIs...
abstract 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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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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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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