Representation Learning: Propositionalization and Embeddings
暫譯: 表示學習：命題化與嵌入技術

表示學習簡介.- 機器學習背景.- 文本嵌入.- 關聯數據的命題化.- 圖形與異質網絡轉換.- 統一表示學習方法.- 表示學習的多面向。

Prof. Nada Lavrač (Jozef Stefan Institute, Slovenia) is Senior researcher at the Department of Knowledge Technologies at JSI (was Head of Department in 2014-2020), and Full Professor at University of Nova Gorica and International Postgraduate School Jozef Stefan (was Vice-Dean in 2016-2020). Her research interests are machine learning, data mining, text mining, knowledge management and computational creativity. She was chair of several conferences ICCC 2014, ILP 2012, AIME 2011, ..., co-chair of conferences including SOKD 2008-2010, ILP 2008, IDA 2007, DS 2006, ..., keynote speaker at KI2020, ADBIS2019, ISWC 2017, LPNMR 2015, JSMI 2014, ... She is/was member of editorial boards of Artificial Intelligence in Medicine, AI Communications, New Generation Computing, Applied AI, Machine Learning Journal and Data Mining and Knowledge Discovery. She is ECCAI/EurAI Fellow, was vice-president of ECCAI (1996-98), and served as member of the International Machine Learning Society board and Artificial Intelligence in Medicine board.
Vid Podpečan, PhD, is a research associate at the Department of Knowledge Technologies at the Jozef Stefan Institute. He obtained his BSc in computer science from the University of Ljubljana in 2007, and his PhD from the Jožef Stefan International Postgraduate School in 2013. His research interests include machine learning, computational systems biology, text mining and natural language processing, and robotics. He co-authored a scientific monograph and published the results of his research in more than 50 scientific publications. He is also actively involved in promoting STEAM with a focus on robotics, programming, and art for which he received an award by the Slovene Science Foundation.
Prof Marko Robnik-Sikonja is Professor of Computer Science and Informatics at University of Ljubljana, Faculty of Computer and Information Science. His research interests span machine learning, data mining, natural language processing, network analytics, and application of data science techniques. His most notable scientific results are from the areas of feature evaluation, ensemble learning, explainable artificial intelligence, data generation, and natural language analytics. He is (co)author of over 150 scientific publications that were cited more than 5,000 times, and three open-source R data mining packages. He participates in several national and international projects, regularly serves as programme committees member of top artificial intelligence and machine learning conferences, and is an editorial board member of seven international journals.

娜達·拉夫拉奇教授（斯洛維尼亞約瑟夫·斯特凡研究所）是約瑟夫·斯特凡研究所知識技術部的高級研究員（2014-2020年擔任部門主任），同時也是新戈里察大學及約瑟夫·斯特凡國際研究生學校的全職教授（2016-2020年擔任副院長）。她的研究興趣包括機器學習、資料探勘、文本探勘、知識管理和計算創造力。她曾擔任多個會議的主席，包括2014年國際計算創造力會議（ICCC）、2012年邏輯編程會議（ILP）、2011年人工智慧醫學會議（AIME）等，並擔任2008-2010年SOKD、2008年ILP、2007年IDA、2006年DS等會議的共同主席。她曾在KI2020、ADBIS2019、ISWC 2017、LPNMR 2015、JSMI 2014等會議中擔任主題演講者。她是《醫學中的人工智慧》、《AI通訊》、《新一代計算》、《應用人工智慧》、《機器學習期刊》和《資料探勘與知識發現》等期刊的編輯委員會成員。她是ECCAI/EurAI的研究員，曾擔任ECCAI的副會長（1996-1998），並曾任國際機器學習學會和醫學中的人工智慧委員會的成員。

維德·波德佩欽博士是約瑟夫·斯特凡研究所知識技術部的研究助理。他於2007年在盧布爾雅那大學獲得計算機科學學士學位，並於2013年在約瑟夫·斯特凡國際研究生學校獲得博士學位。他的研究興趣包括機器學習、計算系統生物學、文本探勘、自然語言處理和機器人技術。他共同撰寫了一本科學專著，並在50多篇科學出版物中發表了他的研究成果。他還積極參與推廣STEAM，專注於機器人技術、程式設計和藝術，並因此獲得斯洛維尼亞科學基金會的獎項。

馬爾科·羅布尼克-西孔雅教授是盧布爾雅那大學計算機與資訊科學系的計算機科學與資訊學教授。他的研究興趣涵蓋機器學習、資料探勘、自然語言處理、網絡分析和數據科學技術的應用。他最顯著的科學成果來自特徵評估、集成學習、可解釋的人工智慧、數據生成和自然語言分析等領域。他是150多篇科學出版物的（共同）作者，這些出版物被引用超過5000次，並且還有三個開源的R資料探勘套件。他參與多個國內外項目，定期擔任頂級人工智慧和機器學習會議的程序委員會成員，並且是七本國際期刊的編輯委員會成員。