02840nam 2200373 450 991041210490332120230831112114.010.1145/3326937(CKB)5280000000242933(NjHacI)995280000000242933(EXLCZ)99528000000024293320230831d2019 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierDLP-KDD '19 Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data /Xiaoqiang Zhu [and eight others]New York, NY :Association for Computing Machinery,2019.1 online resource (109 pages)1-4503-6783-6 In the increasingly digitalized world, it is of utmost importance for various applications to harness the ability to process, understand, and exploit data collected from the Internet. For instance, in customer-centric applications such as personalized recommendation, online advertising, and search engines, interest/intention modeling from customers' behavioral data can not only significantly enhance user experiences but also greatly contribute to revenues. Recently, we have witnessed that Deep Learning-based approaches began to empower these internet- scale applications by better leveraging the massive data. However, the data in these internet-scale applications are high dimensional and extremely sparse, which makes it different from many applications with dense data such as image classification and speech recognition where Deep Learning-based approaches have been extensively studied. For example, the training samples of a typical click-through rate (CTR) prediction task often involve billions of sparse features, how to mine, model and inference from such data becomes an interesting problem, and how to leverage such data in Deep Learning could be a new research direction. The characteristics of such data pose unique challenges to the adoption of Deep Learning in these applications, including modeling, training, and online serving, etc. More and more communities from both academia and industry have initiated the endeavors to solve these challenges. We organized this workshop to provide a venue for both the research and engineering communities to discuss the challenges, opportunities, and new ideas in the practice of Deep Learning on high-dimensional sparse data.Data miningCongressesKnowledge managementCongressesData miningKnowledge management006.312Zhu Xiaoqiang1421484NjHacINjHaclBOOK9910412104903321DLP-KDD '193542886UNINA01453nam 2200469 450 991082317680332120220530212910.084-1324-005-0(CKB)4100000007376915(MiAaPQ)EBC5757278(MiAaPQ)EBC6514440(Au-PeEL)EBL6514440(OCoLC)1080586868(OCoLC)1121518184(FlNmELB)ELB105856(EXLCZ)99410000000737691520220530d2018 uy 0spaurcnu||||||||rdacontentrdamediardacarrierExtranjeros en frontera un estudio jurídico-práctico del reconocimiento, protección y límites del derecho de entrada en España /Susana Cuadrón AmbiteMadrid, Spain :Dykinson,[2018]©20181 online resource (227 páginas)84-9148-949-5 Includes bibliographical references (pages [203]-215).ImmigrantsSpainEmigration and immigration lawSpainSpainfastImmigrantsEmigration and immigration law342.46082Cuadrón Ambite Susana1685752MiAaPQMiAaPQMiAaPQBOOK9910823176803321Extranjeros en frontera4058124UNINA