BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Northeastern University College of Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210910T140000
DTEND;TZID=America/New_York:20210910T150000
DTSTAMP:20260424T041614
CREATED:20210908T154338Z
LAST-MODIFIED:20210908T154338Z
UID:27170-1631282400-1631286000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Wenqian Liu
DESCRIPTION:PhD Dissertation Defense: Explainable Efficient Models for Computer Vision Applications \nWenqian Liu \nLocation: Zoom Link \nAbstract: State of the art deep learning based models\, such as Convolutional Neural Networks (CNNs) and generative models\, achieve impressive results\, but with their great performance comes great complexity and opacity\, huge parametric spaces and little explainability. The criticality of model explainability and output interpretability\, manifests clearly in real-time critical decision making processes and human-centred applications\, such as in healthcare\, security and insurance. Explainability and interpretability are tackled in this thesis\, as intrinsic qualities in the model architecture as well as post-hoc improvement on existing models. In the area of frame prediction in video sequences\, we introduce DYAN\, a novel network with very few parameters\, that is easy to train and produces accurate high quality predictions. Another key aspect of DYAN is interpretability\, as its encoder-decoder architecture is designed following concepts from systems identification theory and exploits the dynamics-based invariants of the data. We also introduce KW-DYAN\, an extension of DYAN that tackles the issue of time lagging in video predictions\, by implementing a novel way of quantifying prediction timeliness and proposing a new recurrent network for adaptive temporal sequence prediction. The experimental results show the reduced lagging across datasets\, while also performing well in other metrics. In this thesis we also propose the first technique to visually explain VAEs by means of gradient-based attentions\, with methods to generate visual attentions from the learned latent space\, and also demonstrate such attention explanations serve more than just explaining VAEs. We show how these attention maps can be used to localize anomalies in images\, conducting state-of-the-art performance on multiple datasets. We also apply our technique for skin image anomaly detection and diagnosis and achieve competitive quantitative and qualitative results.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-wenqian-liu/
END:VEVENT
END:VCALENDAR