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NIPS: Workshop on Action & Interaction 2016 : NIPS 2016 Workshop on Action and Interaction | |||||||||||||
Link: https://sites.google.com/site/nips16interaction/ | |||||||||||||
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Call For Papers | |||||||||||||
We invite you to our workshop on Deep Learning for Action and Interaction at this year's NIPS. The submission deadline has been extended and is now November 8th (23:59 PST). Details are below.
-- NIPS 2016 Workshop on Deep Learning for Action and Interaction Call for Contributions Date: Saturday, December 10th, 2016 Organizers: Chelsea Finn, Raia Hadsell, Dave Held, Sergey Levine, Percy Liang Website: https://sites.google.com/site/nips16interaction/ Abstract ----------- Deep learning systems that act in and interact with an environment must reason about how actions will change the world around them. The natural regime for such real-world decision problems involves supervision that is weak, delayed, or entirely absent, and the outputs are typically in the context of sequential decision processes, where each decision affects the next input. This regime poses a challenge for deep learning algorithms, which typically excel with: (1) large amounts of strongly supervised data and (2) a stationary distribution of independently observed inputs. The algorithmic tools for tackling these challenges have traditionally come from reinforcement learning, optimal control, and planning, and indeed the intersection of reinforcement learning and deep learning is currently an exciting and active research area. At the same time, deep learning methods for interactive decision-making domains have also been proposed in computer vision, robotics, and natural language processing, often using different tools and algorithmic formalisms from classical reinforcement learning, such as direct supervised learning, imitation learning, and model-based control. The aim of this workshop will be to bring together researchers across these disparate fields. The workshopprogram will focus on both the algorithmic and theoretical foundations of decision making and interaction with deep learning, and the practical challenges associated with bringing to bear deep learning methods in interactive settings, such as robotics, autonomous vehicles, and interactive agents. Call for Papers ----------------- We invite the submission of extended abstracts related to machine learning methods for domains involving taking actions and interacting with other agents, including, but not limited to, the following application areas: - robotics - autonomous driving - interactive language and dialog systems - active perception - navigation - game playing Most accepted papers will be presented as posters, but a few selected contributions will be given oral presentations. Accepted papers will be posted in a non-archival format on the workshop website. Abstracts should be 4 pages long (not including references) in NIPS format. Submissions may include a supplement, but reviewers are not required to read any supplementary material. Abstracts should be submitted by November 1st, 2016 by sending an email to nips2016interaction@gmail.com. Submissions may be anonymized or not, at the authors' discretion. Work that has already appeared in a journal, workshop, or conference (including NIPS 2016) must be significantly extended to be eligible forworkshop submission. Work that is currently under review at another venue or has not yet been published in an archival format as of the date of the deadline (Nov 1st) may be submitted. Confirmed Invited Speakers --------------------------------- Pieter Abbeel Abhinav Gupta Tim Lillicrap Joelle Pineau Chris Summerfield Raquel Urtasun Jianxiong Xiao ---------------------------------- Email questions to nips2016interaction@gmail.com |
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