{"id":2888,"date":"2023-11-27T15:21:26","date_gmt":"2023-11-27T15:21:26","guid":{"rendered":"https:\/\/dist2.famnit.upr.si\/?p=2888"},"modified":"2023-12-04T15:22:01","modified_gmt":"2023-12-04T15:22:01","slug":"monday-computer-science-seminar-27-11-2023-online","status":"publish","type":"post","link":"https:\/\/dist.famnit.upr.si\/index.php\/2023\/11\/27\/monday-computer-science-seminar-27-11-2023-online\/","title":{"rendered":"Monday Computer Science Seminar (27.11.2023) &#8211; online"},"content":{"rendered":"<p><span style=\"text-decoration: underline;\">TITLE<\/span>:<br \/>\n<strong>A ResNet Convolutional Neural Network for Time Series data using InceptionNet paradigms<\/strong><\/p>\n<p><span style=\"text-decoration: underline;\">ABSTRACT<\/span>:<br \/>\nThe recent progress in Convolutional Neural Network (CNN) designs has extended their effectiveness to time series data as well. Time series features, which can be represented as one-dimensional vectors, open the door for training deeper networks using ResNet-inspired structures. Inception modules provide the capability to the network to handle objects and patterns of different sizes and capture both local details and global context. We propose an Inception-Residual CNN model tailored for time series data which will be applied on real-life data in retail.<\/p>\n<p><span style=\"text-decoration: underline;\">ABOUT THE PRESENTER<\/span>:<br \/>\nAfter graduating from the Faculty of Computer and Information Science at the University of Ljubljana in 2015, <strong>Vanja Mileski<\/strong> started working at the Jo\u017eef Stefan Institute (JSI). He was a Master&#8217;s student at the International Postgraduate School Jo\u017eef Stefan and a student researcher at the JSI. After finishing his Master&#8217;s studies, he applied his knowledge of data mining in the private sector as a Data Scientist in the retail, telecommunications, banking, stock market and insurance sectors.<br \/>\nHis current research interests include time-series classification, deep learning, ResNet and Inception architectures.<\/p>\n<p>https:\/\/youtu.be\/T5FVL7Xth14<\/p>","protected":false},"excerpt":{"rendered":"<p>TITLE: A ResNet Convolutional Neural Network for Time Series data using InceptionNet paradigms ABSTRACT: The recent progress in Convolutional Neural Network (CNN) designs has extended their effectiveness to [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":2889,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17,16,10,1],"tags":[],"_links":{"self":[{"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/posts\/2888"}],"collection":[{"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/comments?post=2888"}],"version-history":[{"count":1,"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/posts\/2888\/revisions"}],"predecessor-version":[{"id":2890,"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/posts\/2888\/revisions\/2890"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/media\/2889"}],"wp:attachment":[{"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/media?parent=2888"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/categories?post=2888"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dist.famnit.upr.si\/index.php\/wp-json\/wp\/v2\/tags?post=2888"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}