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TZOFFSETFROM:-0500
TZOFFSETTO:-0400
DTSTART:20200308T070000
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TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
DTSTART:20191103T060000
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BEGIN:VEVENT
UID:69f3afe9f30e9
DTSTART;TZID=America/Toronto:20201005T093000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20201005T093000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/phd-defence-c
 omputational-mechanisms-language-understanding
LOCATION:Online 200 University Avenue West Waterloo ON N2L 3G1 Canada
SUMMARY:PhD Defence: Computational Mechanisms of Language Understanding and
 \nUse in the Brain and Behaviour
CLASS:PUBLIC
DESCRIPTION:PLEASE NOTE: THIS PHD DEFENCE WILL BE GIVEN ONLINE.\n\nIVANA KA
 JIĆ\, PHD CANDIDATE\n_David R. Cheriton School of Computer Science_\n\nS
 UPERVISOR: Professor Chris Eliasmith
DTSTAMP:20260430T193922Z
END:VEVENT
BEGIN:VEVENT
UID:69f3afea00afc
DTSTART;TZID=America/Toronto:20200923T130000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200923T130000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-sentiment-lexicon-induction-and
LOCATION:Online 200 University Avenue West Waterloo ON N2L 3G1 Canada
SUMMARY:Master’s Thesis Presentation: Sentiment Lexicon Induction and\nIn
 terpretable Multiple-instance Learning in Financial Markets
CLASS:PUBLIC
DESCRIPTION:PLEASE NOTE: THIS MASTER’S THESIS PRESENTATION WILL BE GIVEN 
 ONLINE.\n\nCHENGYAO FU\, MASTER’S CANDIDATE\n_David R. Cheriton School 
 of Computer Science_\n\nSUPERVISORS: Professors Alan Huang and Yuying Li\n
 \nSentiment analysis has been widely used in the domain of finance.\nThere
  are two most common textual sentiment analysis methods in\nfinance: \\tex
 tit{dictionary-based approach} and \\textit{machine\nlearning approach}.
DTSTAMP:20260430T193922Z
END:VEVENT
BEGIN:VEVENT
UID:69f3afea018ee
DTSTART;TZID=America/Toronto:20200923T110000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200923T110000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-affective-and-human-virtual
LOCATION:Online 200 University Avenue West Waterloo ON N2L 3G1 Canada
SUMMARY:Master’s Thesis Presentation: Affective and Human-Like Virtual\nA
 gents
CLASS:PUBLIC
DESCRIPTION:PLEASE NOTE: THIS MASTER’S THESIS PRESENTATION WILL BE GIVEN 
 ONLINE.\n\nNEIL BUDNARAIN\, MASTER’S CANDIDATE\n_David R. Cheriton Scho
 ol of Computer Science_\n\nSUPERVISOR: Professor Jesse Hoey
DTSTAMP:20260430T193922Z
END:VEVENT
BEGIN:VEVENT
UID:69f3afea024c9
DTSTART;TZID=America/Toronto:20200909T100000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200909T100000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-disentangled-syntax-and
LOCATION:Online 200 University Avenue West Waterloo ON N2L 3G1 Canada
SUMMARY:Master’s Thesis Presentation: Disentangled Syntax and Semantics f
 or\nStylized Text Generation
CLASS:PUBLIC
DESCRIPTION:PLEASE NOTE: THIS MASTER’S THESIS PRESENTATION WILL BE GIVEN 
 ONLINE.\n\nYAO LU\, MASTER’S CANDIDATE\n_David R. Cheriton School of Co
 mputer Science_\n\nSUPERVISOR: Professor Olga Vechtomova
DTSTAMP:20260430T193922Z
END:VEVENT
BEGIN:VEVENT
UID:69f3afea02f91
DTSTART;TZID=America/Toronto:20200505T140000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200505T140000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-obedience-based-multi-agent
LOCATION:Online 200 University Avenue West Waterloo ON N2L 3G1 Canada
SUMMARY:Master’s Thesis Presentation: Obedience-based Multi-Agent\nCooper
 ation for Sequential Social Dilemmas
CLASS:PUBLIC
DESCRIPTION:GAURAV GUPTA\, MASTER’S CANDIDATE\n_David R. Cheriton School
  of Computer Science_\n\nWe propose a mechanism for achieving cooperation 
 and communication in\nMulti-Agent Reinforcement Learning (MARL) settings b
 y intrinsically\nrewarding agents for obeying the commands of other agents
 . At every\ntimestep\, agents exchange commands through a cheap-talk chann
 el.\nDuring the following timestep\, agents are rewarded both for taking\n
 actions that conform to commands received as well as for giving\nsuccessfu
 l commands. We refer to this approach as obedience-based\nlearning.
DTSTAMP:20260430T193922Z
END:VEVENT
BEGIN:VEVENT
UID:69f3afea03b66
DTSTART;TZID=America/Toronto:20200501T133000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200501T133000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-novel-directions-multiagent
LOCATION:Online 200 University Avenue West Waterloo ON N2L 3G1 Canada
SUMMARY:Master’s Thesis Presentation: Novel Directions for Multiagent Tru
 st\nModeling in Online Social Networks
CLASS:PUBLIC
DESCRIPTION:ALEXANDRE PARMENTIER\, MASTER’S CANDIDATE\n_David R. Cherito
 n School of Computer Science_\n\nThis thesis presents two works with the s
 hared goal of improving the\ncapacity of multiagent trust modeling to be a
 pplied to social\nnetworks. 
DTSTAMP:20260430T193922Z
END:VEVENT
BEGIN:VEVENT
UID:69f3afea04645
DTSTART;TZID=America/Toronto:20200415T110000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200415T110000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-asking-help-cost-reinforcement
LOCATION:Online 200 University Avenue West Waterloo ON N2L 3G1 Canada
SUMMARY:Master’s Thesis Presentation: Asking for Help with a Cost in\nRei
 nforcement Learning
CLASS:PUBLIC
DESCRIPTION:COLIN VANDENHOF\, MASTER’S CANDIDATE\n_David R. Cheriton Sch
 ool of Computer Science_\n\nReinforcement learning (RL) is a powerful tool
  for developing\nintelligent agents\, and the use of neural networks makes
  RL techniques\nmore scalable to challenging real-world applications\, fro
 m\ntask-oriented dialogue systems to autonomous driving. However\, one of\
 nthe major bottlenecks to the adoption of RL is efficiency\, as it often\n
 takes many time steps to learn an acceptable policy. 
DTSTAMP:20260430T193922Z
END:VEVENT
BEGIN:VEVENT
UID:69f3afea05139
DTSTART;TZID=America/Toronto:20200319T103000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200319T113000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/ai-seminar-ze
 ro-shot-learning-generalized-information
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West 1304 Waterloo ON N2L 3G1 Canada
SUMMARY:AI Seminar: Zero-Shot Learning: Generalized Information Transfer\nA
 cross Classes
CLASS:PUBLIC
DESCRIPTION:YUHONG GUO\, SCHOOL OF COMPUTER SCIENCE\n_Carleton University_\
 n\nThe need for annotated data is a fundamental bottleneck in developing\n
 automated prediction systems. A key strategy for reducing the reliance\non
  human annotation is to exploit generalized information transfer\,\nwhere 
 a limited data resource is augmented with labeled data collected\nfrom rel
 ated sources. 
DTSTAMP:20260430T193922Z
END:VEVENT
BEGIN:VEVENT
UID:69f3afea061e5
DTSTART;TZID=America/Toronto:20200402T103000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200402T113000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/cancelled-ai-
 seminar-graph-guided-predictions
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West 1304 Waterloo ON N2L 3G1 Canada
SUMMARY:CANCELLED • AI Seminar: Graph Guided Predictions
CLASS:PUBLIC
DESCRIPTION:VIKAS GARG\, ELECTRICAL ENGINEERING & COMPUTER SCIENCE\n_Massac
 husetts Institute of Technology_\n\nIn this talk I will describe our recen
 t work on effectively using\ngraph structured data. Specifically\, I will 
 discuss how to compress\ngraphs to facilitate predictions\, understand the
  capacity of\nalgorithms operating on graphs\, and how to infer interactio
 n graphs so\nas to predict deliberative outcomes.
DTSTAMP:20260430T193922Z
END:VEVENT
BEGIN:VEVENT
UID:69f3afea0727b
DTSTART;TZID=America/Toronto:20200312T103000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200312T113000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/ai-seminar-de
 ep-learning-graphs
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West 1304 Waterloo ON N2L 3G1 Canada
SUMMARY:AI Seminar: Deep Learning on Graphs
CLASS:PUBLIC
DESCRIPTION:RENJIE LIAO\, DEPARTMENT OF COMPUTER SCIENCE\n_University of To
 ronto_\n\nGraphs are ubiquitous in many domains like computer vision\, nat
 ural\nlanguage processing\, computational chemistry\, and computational so
 cial\nscience. Although deep learning has achieved tremendous success\,\ne
 ffectively handling graphs is still challenging due to their discrete\nand
  combinatorial structures. In this talk\, I will discuss my recent\nwork w
 hich improves deep learning on graphs from both modeling and\nalgorithmic 
 perspectives.
DTSTAMP:20260430T193922Z
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