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Improving the clarity of questions in Community Question Answering networks
Every day, thousands of questions are asked on the Community Question Answering network, making these questions and answers extremely valuable for...
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Finite-time error bounds for Greedy-GQ
Greedy-GQ with linear function approximation, originally proposed in Maei et al. (in: Proceedings of the international conference on machine learning...
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SWoTTeD: an extension of tensor decomposition to temporal phenoty**
Tensor decomposition has recently been gaining attention in the machine learning community for the analysis of individual traces, such as Electronic...
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Semantic-enhanced graph neural networks with global context representation
Node classification is a crucial task for efficiently analyzing graph-structured data. Related semi-supervised methods have been extensively studied...
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Explaining Siamese networks in few-shot learning
Machine learning models often struggle to generalize accurately when tested on new class distributions that were not present in their training data....
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A model of the relationship between the variations of effectiveness and fairness in information retrieval
The requirement that, for fair document retrieval, the documents should be ranked in the order to equally expose authors and organizations has been...
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An interpretable model for sepsis prediction using multi-objective rule extraction
Sepsis is a leading cause of death among intensive care unit patients. Early sepsis prediction, which primarily relies on advanced artificial...
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From MNIST to ImageNet and back: benchmarking continual curriculum learning
Continual learning (CL) is one of the most promising trends in recent machine learning research. Its goal is to go beyond classical assumptions in...
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What word-prosodic typology is missing: Motivating foot structure as an analytical tool for syllable-internal prosodic oppositions
A notoriously contested subarea of phonological typology is word-prosodic typology, which governs suprasegmental structure (such as tone, syllable...
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Reversible jump attack to textual classifiers with modification reduction
Recent studies on adversarial examples expose vulnerabilities of natural language processing models. Existing techniques for generating adversarial...
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A survey on interpretable reinforcement learning
Although deep reinforcement learning has become a promising machine learning approach for sequential decision-making problems, it is still not mature...
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PolieDRO: a novel classification and regression framework with non-parametric data-driven regularization
PolieDRO is a novel analytics framework for classification and regression that harnesses the power and flexibility of data-driven distributionally...
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Application of supervised machine learning methods in injection molding process for initial parameters setting: prediction of the cooling time parameter
The injection molding process is considered as one of the most used process in the plastics industry due to its reliability and its profitability;...
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Spreading and correspondence in Huave vowel copy
Assimilation is a central phenomenon in phonology, yet there is little consensus on either its representation or computation. In particular, the...
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Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling
There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in...
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Relation representation based on private and shared features for adaptive few-shot link prediction
Although Knowledge Graphs (KGs) provide great value in many applications, they are often incomplete with many missing facts. KG Completion (KGC) is a...
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Exposing and explaining fake news on-the-fly
Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the...
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Utilizing reinforcement learning for de novo drug design
Deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years....
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Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach
Deep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation...
