Titlebook: Artificial Intelligence and Machine Learning; 33rd Benelux Confere Luis A. Leiva,Cédric Pruski,Christoph Schommer Conference proceedings 20

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Active Learning for Reducing Labeling Effort in Text Classification Tasks on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on AL in a text classification setting and next to none has involved the more recent, state-of-the-art

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Self-labeling of?Fully Mediating Representations by?Graph Alignment interested to learn . where we have a fully mediating representation . such that . factors into .. However, observing V requires detailed and expensive labels. We propose . approach that generates rich or detailed labels given normal labels .. In this paper we investigate the scenario of domain ada

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Task Independent Capsule-Based Agents for Deep Q-Learningstics towards pose and lighting. They have been proposed as an alternative to relational insensitive and translation invariant Convolutional Neural Networks (CNN). It has been empirically proven that CapsNets are capable of achieving competitive performance while requiring significantly fewer parame

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The Effect of Noise Level on the Accuracy of Causal Discovery Methods with Additive Noise Modelsause-effect pairs. These methods also proved their ability to successfully determine the direction of causal relationships from observational real-world data. Yet in bivariate situations, causal discovery problems remain challenging. A class of methods, that also allows tackling the bivariate case,

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A Bayesian Framework for?Evaluating Evolutionary Arthese methods remains an open question, due to the subjective nature of the domain. In this work, we propose a framework for evaluating evolutionary art using a Bayesian approach..The framework provides a method to analyse the results of a number of ‘a(chǎn)rt Turing tests’ (ATTs) with a Bayesian model com

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Dutch SQuAD and?Ensemble Learning for?Question Answering from?Labour Agreements work flexibility are conducted on the regular basis by means of specialised questionnaires. We show that a relatively small domain-specific dataset allows to train the state-of-the-art extractive question answering (QA) system to answer these questions automatically. This paper introduces the new d

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