Titlebook: Machine Learning, Optimization, and Data Science; 6th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202

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Methods for Hyperparameters Optimization in Learning Approaches: An Overview,arning methods exists, ranging from biologically inspired neural networks to statistical methods. A common trait in these methods is that they are parameterized by a set of hyperparameters, which must be set appropriately by the user to maximize the usefulness of the learning approach. In this paper

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,Sparse Consensus Classification for?Discovering Novel Biomarkers in?Rheumatoid Arthritis,e usually treated with anti-tumor necrosis factor (anti-TNF), which in certain cases do not contribute to reach remission. Consequently, there is a need to develop models that can predict therapy response, thus preventing disability, maintain life quality, and decrease cost treatment. Transcriptomic

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,Learning More Expressive Joint Distributions in Multimodal Variational?Methods,res larger expressive power to capture high-level concepts and provide better data representations. However, multimodal generative models based on variational inference are limited due to the lack of flexibility of the approximate posterior, which is obtained by searching within a known parametric f

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A Stochastic Optimization Model for Frequency Control and Energy Management in a Microgrid,icrogrid in Finland. The BESS is used to assist the joint optimization of self-consumption and revenues provided by selling primary frequency reserves in the normal Frequency Containment Reserve (FCR) market. To participate in this frequency control market, a day-ahead planning of the reserve that w

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