Continuously Reproducing Toolchains in Pattern Recognition and Machine Learning Experiments
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Deep neural networks have been empirically successful in a variety of tasks, however their theoretical understanding is still poor. In particular, modern deep neural networks have many more parameters than training data. Thus, in principle they should over ...
The study of how people schedule their daily activities is of interest in the context of transport demand forecasting using activity based-models, where activity schedules are generated in order to estimate the trip demand they produce. Machine learning ha ...
This dataset contains a collection of ancillary data that were used for the publication Frequent new particle formation over the high Arctic pack ice by enhanced iodine emissions (Baccarini et al., 2020, Nature Comm.). Some of these datasets can be reprodu ...
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Gamification aims to enhance the educational experience by enabling students to have fun with technology. Although research in the field has previously looked into the effectiveness of gamification, reviews of existing studies show that in the context of e ...
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Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyse ...