The workshop venue is the Hyatt Beacon Ballroom D+E+F+H.
Introduction & Applications
Deep & Representation Learning
Call for papersExtreme classification is a rapidly growing research area focussing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modelling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc. Extreme classification has also opened up a new paradigm for ranking and recommendation by reformulating them as multi- label learning tasks where each item to be ranked or recommended is treated as a separate label. Such reformulations have led to significant gains over traditional collaborative filtering and content based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in industry.
Extreme classification raises a number of interesting research questions including those related to:
The workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. In particular, we aim to bring together researchers from the natural language processing, computer vision and core machine learning communities to foster interaction and collaboration. Several leading researchers will present invited talks detailing the latest advances in the area. We also seek extended abstracts presenting work in progress which will be reviewed for acceptance as a spotlight + poster or a talk. The workshop should be of interest to researchers in core supervised learning as well as application domains such as recommender systems, computer vision, computational advertising, information retrieval and natural language processing. We expect a healthy participation from both industry and academia.
Submission informationPlease submit extended abstracts/full papers at XC17 by 15th October 2017.
LinksExtreme Classification Resources
NIPS 2016 Extreme Classification Workshop