Jul 17, 2020
In this talk we propose the DeepXML framework for deep extreme multi-label learning and apply it to short-text document classification. We demonstrate that DeepXML can: (a) be used to analyze seemingly disparate deep extreme classifiers; (b) can lead to improvements in leading algorithms such as XML-CNN & MACH when they are recast in the proposed framework; and (c) can lead to a novel algorithm called Astec which can be up to 12% more accurate and up to 40x faster to train than the state-of-the-art for short text document classification. Finally, we show that when flighted on Bing, Astec can be used for personalized search, ads and recommendation for billions of users. Astec can handle billions of events per day, can process more than a hundred thousand events per second and leads to a significant improvement in key metrics as compared to state-of-the-art methods in production.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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