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SUMMARY:Dynamic-graph representation models for seizure prediction from EE
 G
DTSTART:20220915T100000
DTEND:20220915T120000
DTSTAMP:20260407T152654Z
UID:488e97182921ec57eab6c0a5149a2434d1a3ecaca79c615863f6f44f
CATEGORIES:Conferences - Seminars
DESCRIPTION:William Cappelletti\nEDIC candidacy exam\nExam president: Prof
 . Mahsa Shoaran\nThesis advisor: Prof. Pascal Frossard\nCo-examiner: Prof.
  Pierre Vandergheynst\n\nAbstract\ncoming soon\n\nBackground papers\n- C. 
 Shang\, J. Chen\, and J. Bi\, “Discrete Graph Structure Learning for For
 ecasting Multiple Time Series\,” presented at the International Conferen
 ce on Learning Representations\, Feb. 2022. Available: https://openreview
 .net/forum?id=WEHSlH5mOk\n- K. Yamada and Y. Tanaka\, “Temporal Multires
 olution Graph Learning\,” IEEE Access\, vol. 9\, pp. 143734–143745\, 2
 021\, doi: 10.1109/ACCESS.2021.3120994.\n- S. Tang et al\, “Self-Supervi
 sed Graph Neural Networks for Improved Electroencephalographic Seizure Ana
 lysis\,” presented at the International Conference on Learning Represent
 ations\, Sep. 2021. Available: https://openreview.net/forum?id=k9bx1EfHI_-
 \n 
LOCATION:ELE 242 https://plan.epfl.ch/?room==ELE%20242
STATUS:CONFIRMED
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