BEGIN:VCALENDAR
VERSION:2.0
PRODID:-// - ECPv6.15.16//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.neuropac.info
X-WR-CALDESC:Events for 
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20220313T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20221106T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20230312T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20231105T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20240310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20241103T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230618
DTEND;VALUE=DATE:20230623
DTSTAMP:20260502T032551
CREATED:20230129T222319Z
LAST-MODIFIED:20230129T222319Z
UID:10000023-1687046400-1687478399@www.neuropac.info
SUMMARY:Computer Vision and Pattern Recognition Conference (CVPR) 2023
DESCRIPTION:The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost\, it provides an exceptional value for students\, academics and industry researchers.
URL:https://www.neuropac.info/event/computer-vision-and-pattern-recognition-conference-cvpr-2023/
LOCATION:Vancouver Convention Center\, Vancouver\, Canada
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230618
DTEND;VALUE=DATE:20230624
DTSTAMP:20260502T032551
CREATED:20230129T221059Z
LAST-MODIFIED:20230129T221059Z
UID:10000021-1687046400-1687564799@www.neuropac.info
SUMMARY:International Joint Conference on Neural Networks (IJCNN)
DESCRIPTION:The International Joint Conference on Neural Networks is organized jointly by the International Neural Network Society and the IEEE Computational Intelligence Society\, and is the premiere international meeting for researchers and other professionals in neural networks and related areas. \nEach year\, the conference features invited plenary talks by world-renowned speakers in the areas of neural network theory and applications\, computational neuroscience\, robotics\, and distributed intelligence. In addition to regular technical sessions with oral and poster presentations\, the conference program will include special sessions\, competitions\, tutorials\, and workshops on topics of current interest.
URL:https://www.neuropac.info/event/international-joint-conference-on-neural-networks-ijcnn/
LOCATION:Gold Coast Convention and Exhibition Centre\, Queensland\, Australia
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230619
DTEND;VALUE=DATE:20230620
DTSTAMP:20260502T032551
CREATED:20230129T163545Z
LAST-MODIFIED:20230129T163545Z
UID:10000019-1687132800-1687219199@www.neuropac.info
SUMMARY:Workshop on Event-based Vision @ CVPR 2023
DESCRIPTION:4th International Workshop on Event-Based Vision. \nHeld in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition 2023\, as part of the track: CV for non-traditional modalities\n\nThis workshop is dedicated to event-based cameras\, smart cameras\, and algorithms processing data from these sensors. Event-based cameras are bio-inspired sensors with the key advantages of microsecond temporal resolution\, low latency\, very high dynamic range\, and low power consumption. Because of these advantages\, event-based cameras open frontiers that are unthinkable with standard frame-based cameras (which have been the main sensing technology for the past 60 years). These revolutionary sensors enable the design of a new class of algorithms to track a baseball in the moonlight\, build a flying robot with the agility of a bee\, and perform structure from motion in challenging lighting conditions and at remarkable speeds. These sensors became commercially available in 2008 and are slowly being adopted in computer vision and robotics. In recent years they have received attention from large companies\, e.g.\, the event-sensor company Prophesee collaborated with Intel and Bosch on a high spatial resolution sensor\, Samsung announced mass production of a sensor to be used on hand-held devices\, and they have been used in various applications on neuromorphic chips such as IBM’s TrueNorth and Intel’s Loihi. The workshop also considers novel vision sensors\, such as pixel processor arrays (PPAs)\, which perform massively parallel processing near the image plane. Because early vision computations are carried out on-sensor\, the resulting systems have high speed and low-power consumption\, enabling new embedded vision applications in areas such as robotics\, AR/VR\, automotive\, gaming\, surveillance\, etc. This workshop will cover the sensing hardware\, as well as the processing and learning methods needed to take advantage of the above-mentioned novel cameras.
URL:https://www.neuropac.info/event/workshop-on-event-based-vision-cvpr-2023/
LOCATION:Vancouver\, Canada\, Vancouver\, Canada
CATEGORIES:Conference,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230620T080000
DTEND;TZID=America/Los_Angeles:20230620T090000
DTSTAMP:20260502T032551
CREATED:20230618T010420Z
LAST-MODIFIED:20230618T010420Z
UID:10000237-1687248000-1687251600@www.neuropac.info
SUMMARY:INRC Forum: Wolfgang Maass\, Christoph Stoeckl & Yukun Yang
DESCRIPTION:Local prediction-learning in high-dimensional spaces enables neural networks to plan\nAbstract: Being able to plan a sequence of actions in order to reach a goal\, or more generally to solve a problem\, is a cornerstone of higher brain function. But compelling models which explain how the brain can achieve that are missing. We show that local synaptic plasticity enables a neural network to create high-dimensional representations of actions and sensory inputs so that they encode salient information about their relationship. In fact\, it can create a cognitive map that reduces planning to a simple geometric problem in a high-dimensional space that can easily be solved by a neural network. This method also explains how self-supervised learning enables a neural network to control a complex muscle system so that it can handle locomotion challenges that never occurred during learning. The underlying learning strategy bears some similarity to self-attention networks (Transformers). But it does not require non-local learning rules or very large datasets. Hence it is suitable for implementation in highly energy-efficient neuromorphic hardware\, in particular for on-chip learning on Loihi 2.\nOne goal of our presentation will be to initiate discussions about the relation of this learning-based use of large vectors to other VSA approaches\, its relation to Transformers\, and possible applications in robotics. \nBio: Wolfgang Maass is a Professor of Computer Science at Technische Universität Graz. He received his PhD (1974) and Habilitation (1978) in Mathematics from Ludwig-Maximilians-Universität in Munich. He conducted research at MIT\, the University of Chicago\, and UC Berkeley\, as a Heisenberg Fellow of the Deutsche Forschungsgemeinschaft. He has been the Editor of Machine Learning (1995-1997)\, Archive for Mathematical Logic (1987-2000)\, and Biological Cybernetics (2006-present). He was also a Sloan Fellow at the Computational Neurobiology Lab of the Salk Institute in La Jolla\, California from 1997-1998. Since 2005\, he has been an Adjunct Fellow of the Frankfurt Institute of Advanced Studies (FIAS).\nChristoph Stoeckl is a Postdoc researcher at Technische Universität Graz working in the intersection between computational neuroscience and AI. His research interests include neuromorphic hardware as well as exploring connections between Transformers and neural networks. Before joining the research lab of Prof. Maass\, he obtained a Master’s degree in Computer Science also at TU Graz.\nYukun Yang is a 1st-year Doctoral Student at Technische Universität Graz\, supervised by Prof. Wolfgang Maass. His primary research interest is at the intersection of AI and neuroscience\, with a focus on discovering the learning principles of the brain and its neuromorphic applications. Before joining TU Graz\, he earned M.S. in the ECE Department at Duke University in 2020. Earlier\, he received B.E. in Information Engineering from Xi’an Jiaotong University in 2018. \nFor the meeting link\, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).
URL:https://www.neuropac.info/event/inrc-forum-tu-graz/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230625
DTEND;VALUE=DATE:20230715
DTSTAMP:20260502T032551
CREATED:20230129T155215Z
LAST-MODIFIED:20230129T155215Z
UID:10000012-1687651200-1689379199@www.neuropac.info
SUMMARY:Telluride Neuromorphic Cognition Engineering Workshop
DESCRIPTION:The workshop is a 3-week project based meeting organized around specific topic areas to bring the organizing principles of neural cognition into machine intelligence\, and to use lessons and technology from machine intelligence to understand how brains work. \n 
URL:https://www.neuropac.info/event/telluride-neuromorphic-cognition-engineering-workshop/
LOCATION:TBA
CATEGORIES:Workshop
END:VEVENT
END:VCALENDAR