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  • title: Learning From Irregularly-Sampled Time Series: A Missing Data Perspective
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            Learning From Irregularly-Sampled Time Series: A Missing Data Perspective
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            Learning From Irregularly-Sampled Time Series: A Missing Data Perspective

            Jul 12, 2020

            Speakers

            BM

            Benjamin Marlin

            Speaker · 1 follower

            SCL

            Steven Cheng-Xian Li

            Speaker · 0 followers

            About

            Irregularly-sampled time series occur in many domains including healthcare. They can be challenging to model because they do not naturally yield a fixed-dimensional representation as required by many standard machine learning models. In this paper, we consider irregular sampling from the perspective of missing data. We model observed irregularly sampled time series data as a sequence of index-value pairs sampled from a continuous but unobserved function. We introduce an encoder-decoder framework…

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            ICML 2020

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            About ICML 2020

            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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