ICML 2019
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Workshop on Self-Supervised Learning

Aaron van den Oord · Yusuf Aytar · Carl Doersch · Carl Vondrick · Alec Radford · Pierre Sermanet · Amir Zamir · Pieter Abbeel

Grand Ballroom A

Self-supervised learning is a promising alternative where proxy tasks are developed that allow models and agents to learn without explicit supervision in a way that helps with downstream performance on tasks of interest. One of the major benefits of self-supervised learning is increasing data efficiency: achieving comparable or better performance with less labeled data or fewer environment steps (in Reinforcement learning / Robotics).
The field of self-supervised learning (SSL) is rapidly evolving, and the performance of these methods is creeping closer to the fully supervised approaches. However, many of these methods are still developed in domain-specific sub-communities, such as Vision, RL and NLP, even though many similarities exist between them. While SSL is an emerging topic and there is great interest in these techniques, there are currently few workshops, tutorials or other scientific events dedicated to this topic.
This workshop aims to bring together experts with different backgrounds and applications areas to share inter-domain ideas and increase cross-pollination, tackle current shortcomings and explore new directions. The focus will be on the machine learning point of view rather than the domain side.


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Timezone: America/Los_Angeles


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