Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track; European Conference, Albert Bifet,Povilas Daniu?is,In

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Clemens Damke,Eyke Hüllermeier the years in attempts to capture and maintain the attention of students in my courses. I will also describe how I try to utilize the wealth of expertise in the classroom to enhance the course experience for students and for myself. I teach courses on Consumer Behavior, Digital Marketing, Advertisin

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Yuhan Ye,Jingbo Zhou,Shuangli Li,Congxi Xiao,Haochao Ying,Hui Xiong an easily understandable form including their development from ancient history through the life and times of J. S. Bach, making connections between science, philosophy, art, architecture, particle physics, cal978-3-030-63771-2978-3-030-63769-9Series ISSN 2199-0956 Series E-ISSN 2199-0964

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A Unified Contrastive Loss for?Self-traininglied to popular self-training methods, results in significant performance improvements across three different datasets with a limited number of labeled data. Additionally, we demonstrate further improvements in convergence speed, transfer ability, and hyperparameter stability. The code is available

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Hierarchical Structure-Aware Graph Prompting for?Drug-Drug Interaction Predictionask into a uniform task format. This is achieved through an adaptive dual-level prompting process featuring unique virtual tokens. Aligned with our hierarchical structure-aware pre-training, it effectively activates relevant knowledge for DDI prediction, fostering a more seamless integration between

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Employing Two-Dimensional Word Embedding for?Difficult Tabular Data Stream Classificationng the . algorithm and then performs a single ResNet-18 training epoch. Experiments conducted on synthetic and real data streams have demonstrated the ability of . to achieve classification quality statistically significantly superior to state-of-the-art algorithms while maintaining comparable proce

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Univariate Skeleton Prediction in?Multivariate Systems Using Transformersulti-Set Skeleton Prediction and outputs a univariate symbolic skeleton. Thus, such skeletons represent explanations of the function approximated by the regression NN. Experimental results demonstrate that this method learns skeleton expressions matching the underlying functions and outperforms two

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