Multi-Task Learning in the Wilderness

Jun 15, 2019

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Driven by progress in deep learning, the machine learning community is now able to tackle increasingly more complex problems—ranging from multi-modal reasoning to dexterous robotic manipulation—all of which typically involve solving nontrivial combinations of tasks. Thus, designing adaptive models and algorithms that can efficiently learn, master, and combine multiple tasks is the next frontier. AMTL workshop aims to bring together machine learning researchers from areas ranging from theory to applications and systems, to explore and discuss: * advantages, disadvantages, and applicability of different approaches to learning in multitask settings, * formal or intuitive connections between methods developed for different problems that help better understand the landscape of multitask learning techniques and inspire technique transfer between research lines, * fundamental challenges and open questions that the community needs to tackle for the field to move forward.

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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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