Since 2010, the year of initiation of annual Imagenet Competition where research teams submit programs that classify and detect objects, machine learning has gained significant popularity. In the present age, Machine learning, in particular deep learning, is incredibly powerful to make predictions based on large amounts of available data. There are many applications of machine learning in Computer vision, pattern recognition including Document analysis, Medical image analysis etc. In order to facilitate innovative collaboration and engagement between document analysis community and other research communities like computer vision and images analysis etc. here we plan to organize a workshop of Machine learning before the ICDAR conference.
The topics of interest of this workshop include, but are not limited to:
Title: Deep learning with noisy supervision.
Abstract: Central to many state-of-the-art classification systems via deep learning is sufficient accurate annotations for training. This is almost the bottleneck of all machine learning algorithms deployed with deep neural networks. The dilemma behind such a phenomenon is essentially the trade-off between the low expensive model design and the low expensive sample collection. For practical purposes to alleviate this issue, learning with noisy supervision is a critical solution in the Big Data era, since the noisily annotated data on the social websites and Amazon Mechanical Turk platforms can be easily acquired. In this talk, I will explore several strategies to solve the fundamental problems when training deep neural networks with noisy supervision.
Biography: Ivor W Tsang is an ARC Future Fellow and Professor of Artificial Intelligence, at University of Technology Sydney (UTS). He is also the Research Director of the UTS Flagship Research Centre for Artificial Intelligence (CAI) with more than 30 faculty members and 180 PhD students. His research focuses on transfer learning, feature selection, crowd intelligence, big data analytics for data with extremely high dimensions in features, samples and labels. He has more than 180 research papers published in top-tier journal and conference papers. According to Google Scholar, he has more than 12,000 citations and his H-index is 54. In 2009, Prof Tsang was conferred the 2008 Natural Science Award (Class II) by Ministry of Education, China, which recognized his contributions to kernel methods. In 2013, Prof Tsang received his prestigious Australian Research Council Future Fellowship for his research regarding Machine Learning on Big Data. In addition, he had received the prestigious IEEE Transactions on Neural Networks Outstanding 2004 Paper Award in 2007, the 2014 IEEE Transactions on Multimedia Prize Paper Award, and a number of best paper awards and honors from reputable international conferences, including the Best Student Paper Award at CVPR 2010. He serves as an Associate Editor for the IEEE Transactions on Big Data, the IEEE Transactions on Emerging Topics in Computational Intelligence and Neurocomputing. He is serving as a Guest Editor for the special issue of "Structured Multi-output Learning: Modelling, Algorithm, Theory and Applications" in the IEEE Transactions on Neural Networks and Learning Systems. He serves as an Area Chair/Senior PC for NeurIPS, AISTATS, AAAI and IJCAI.
ICDAR-WML 2019 will follow a single blind review process. Authors may include their names and affiliations in the manuscript.
Paper Format and Length
Papers should be formatted with the style files/details available in the IEEE paper formatting template. Papers accepted for the conference will be allocated 6 pages in the proceedings, with the option of purchasing up to 2 extra pages for AUD 100 per page. This will have to be paid after paper acceptance and at the time of registration. The length of the submitted manuscript should match that intended for final publication. Therefore, if you are unwilling or unable to pay the extra charge you should limit yourself to 6 pages. Otherwise the page limit is 8 pages.
All camera ready submissions and IEEE copyright form will be handled electronically via the CPS Website ( https://ieeecps.org/#!/auth/login?ak=1&pid=6I1Fdb6d1mA5MeDNOVruyL ).
The due date of camera-ready submission is extended to Aug. 7th, 2019.
Please note the following when you submit the papers.
1) During the submission process, the system requests you to enther the paper ID. Please enter the abbreviation of workshop name followed by the paper ID on the initial submission in the form.
2) If you have already submitted your paper in the submission page for main conference which opened formerly, please resubmit your paper using this link.
For any other information you may contact ICDAR WML 2019 Secretary by email at firstname.lastname@example.org or ICDAR WML 2019 chair by email at email@example.com