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SUMMARY:Thursday Morning Talk with  Rico Jonschkowski (Google Brain): Perception in Motion
DESCRIPTION:Title: Perception in Motion\nAbstract: This is a talk on perception in two parts. Part one exemplifies the “movement” of the field of learning-based robot perception. Here\, I will give one example for increasing structural assumptions and one for decreasing them based on our work on differentiable mapping and differentiable resampling. Part two takes the title more literally and investigates the role of motion for learning robotic perception. In that part\, I will talk about using motion as a cue for unsupervised learning of optical flow\, ego-motion estimation\, monocular depth estimation\, and object detection. \nBio: Rico Jonschkowski is a research scientist at Robotics at Google\, working on unsupervised learning for robot perception. His research vision is to identify structural invariances from the perspective of a learning agent in our world and to inject those invariances as priors into learning algorithms to make robot learning more efficient. Before joining Google in 2018\, Rico received his Dr. rer. nat. (German PhD equivalent) from Technische Universität Berlin and his MSc and BSc from Freie Universität Berlin. He was part of the RBO team that won the Amazon Picking Challenge 2015 and received the best systems paper award at Robotics: Science and Systems (RSS) in 2016. \n  \n(Photo by Franck V. on Unsplash)
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-with-rico-jonschkowski-google-brain-perception-in-motion/
LOCATION:On ZOOM (Contact us for Link)
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DTSTART;TZID=Europe/Berlin:20200910T100000
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CREATED:20200827T080235Z
LAST-MODIFIED:20250604T100036Z
UID:8582-1599732000-1599735600@www.scienceofintelligence.de
SUMMARY:Thursday Morning Talk With Benjamin Wild: Social Networks Through Time – Individuality in a Colony of Honey Bees
DESCRIPTION:ABSTRACT:\nIn many social systems\, an individual’s role is reflected by its interactions with other members of the group. In many model organisms\, and particularly in social insects\, the patterns of actions and interactions among individuals are not static but constantly evolving over time. This can be due to the emergence or demise of certain individuals\, changing task allocation because of temporal polyethism and changes in the environment\, or many other reasons. Understanding such temporal patterns in complex social networks remains a challenging problem. In this talk\, I will present two recent approaches we have developed to extract meaningful and inherently interpretable embeddings of the social behavior of honey bees from temporal interaction matrices. The embeddings allow us to describe an individual’s role in the colony at any point during her life\, to detect clusters of social development of individuals\, to compare the structure of the networks at different times\, and to compare the role of individuals in the social structure in a meaningful way even when they were never alive at the same time. \n  \n(Photo by Nathaniel Sison on Unsplash) \n 
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-with-benjamin-wild/
LOCATION:On ZOOM (Contact us for Link)
ATTACH;FMTTYPE=image/jpeg:https://www.scienceofintelligence.de/wp-content/uploads/2020/08/nathaniel-sison-WxsKToO4iXs-unsplash-scaled-1.jpg
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