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X-WR-CALNAME:Northeastern University College of Engineering
X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;TZID=America/New_York:20230428T083000
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DTSTAMP:20260531T055744
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UID:36629-1682670600-1682674200@coe.northeastern.edu
SUMMARY:GSE Wonder Week Series: Learn more about Chemical Engineering Graduate Programs
DESCRIPTION:Join us to learn more about the Graduate Programs offered by the Department of Chemical Engineering. \nRegister
URL:https://coe.northeastern.edu/event/gse-wonder-week-series-learn-more-about-chemical-engineering-graduate-programs/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
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DTSTART;TZID=America/New_York:20230428T100000
DTEND;TZID=America/New_York:20230428T104500
DTSTAMP:20260531T055744
CREATED:20230413T210640Z
LAST-MODIFIED:20230413T210640Z
UID:36684-1682676000-1682678700@coe.northeastern.edu
SUMMARY:GSE Wonder Wonder Week Series: Learn More About Graduate Co-op for Multidisciplinary Engineering Programs
DESCRIPTION:Join us to learn more about co-op experiences available to those who enroll in our Graduate Multidisciplinary Engineering Programs! \nRegister
URL:https://coe.northeastern.edu/event/gse-wonder-wonder-week-series-learn-more-about-graduate-co-op-for-multidisciplinary-engineering-programs/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230428T110000
DTEND;TZID=America/New_York:20230428T114500
DTSTAMP:20260531T055744
CREATED:20230413T210558Z
LAST-MODIFIED:20230413T210558Z
UID:36686-1682679600-1682682300@coe.northeastern.edu
SUMMARY:GSE Wonder Week Series: Learn More About Our Graduate Multidisciplinary Engineering Programs
DESCRIPTION:Join us to learn more about our Graduate Multidisciplinary Engineering Programs including: Data Architecture &n Management\, Information Systems\, Information Systems – Bridge\, and Software Engineering Systems. \nRegister
URL:https://coe.northeastern.edu/event/gse-wonder-week-series-learn-more-about-our-graduate-multidisciplinary-engineering-programs/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230428T120000
DTEND;TZID=America/New_York:20230428T124500
DTSTAMP:20260531T055744
CREATED:20230413T210429Z
LAST-MODIFIED:20230413T210457Z
UID:36688-1682683200-1682685900@coe.northeastern.edu
SUMMARY:GSE Wonder Week Series: Learn More About Graduate Co-operative Education - Disciplinary Engineering Programs
DESCRIPTION:Join us to learn more about co-op opportunities available in our Graduate Disciplinary Engineering Programs. \nRegister
URL:https://coe.northeastern.edu/event/gse-wonder-week-series-learn-more-about-graduate-co-operative-education-disciplinary-engineering-programs/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230428T130000
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DTSTAMP:20260531T055744
CREATED:20230426T134141Z
LAST-MODIFIED:20230426T134141Z
UID:36838-1682686800-1682694000@coe.northeastern.edu
SUMMARY:Balaji Sundareshan's MS Thesis Defense
DESCRIPTION:“Cross-View Action Recognition using Transformers” \nCommittee Members:\n1. Prof. Octavia Camps (Advisor)\n2. Prof. Mario Sznaier\n3. Prof. Huaizu Jiang \nAbstract:\nCross-view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint. This is a challenging problem since the appearance of action changes significantly with the viewpoint. Applications of CVAR include surveillance and monitoring of assisted living facilities where is not practical or feasible to collect large amounts of training data when adding a new camera. In this thesis\, we propose a method to perform cross-view action recognition from 2D skeleton data using Transformers. First\, we understand the interpretability of the basline network and its submodules by visualizing the saliency map. Next\, we integrate Transformers at different parts of the network for both single-clip and multi-clip and understand the impact on the performance. In the end\, we also discuss the necessity of pretraining sub-modules in the network and their impact on the performance.
URL:https://coe.northeastern.edu/event/balaji-sundareshans-ms-thesis-defense/
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