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            Transfer and Multitask Learning
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            Transfer and Multitask Learning

            Jun 12, 2019

            Speakers

            AE

            Adam Earle

            Speaker · 0 followers

            AT

            Ameet Talwalkar

            Speaker · 2 followers

            ACS

            Asa Cooper Stickland

            Speaker · 0 followers

            About

            Domain Agnostic Learning with Disentangled Representations Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains. Yet the current literature assumes that the separation of target data into distinct domains is known a priori. In this paper, we propose the task of Domain-Agnostic Learning (DAL): How to transfer knowledge from a labeled source domain to unlabeled data from arbitrary target domains? To tackle this problem, we devise a n…

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            About ICML 2019

            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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