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DSI Webinar

26 Feb

SPS-DSI (DEGAS) Webinar: Understanding and Improving Graph Neural Nets With the cSBM

Date: 26 February 2025
Time: 10:00 AM ET (New York time)
Presenter(s): Ivan Dokmanić

Website Link: 
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22 Jan

SPS-DSI (DEGAS) Webinar: Demystifying and Mitigating Unfairness for Learning over Graphs

Date: 22 January 2025
Time: 10:00 AM ET (New York time)
Presenter(s): Yanning Shen

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04 Dec

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SPS-DSI (DEGAS) Webinar: SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups

Date: 4 December 2024
Time: 11:00 AM ET (New York time)
Presenter(s): Renjie Liao

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20 Nov

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SPS-DSI (DEGAS) Webinar: Vertex, Edge, Clique: What's in a Graph?

Date: 20 November 2024
Time: 2:00 PM (Paris Time)
Presenter(s): Bastian Grossenbacher Rieck

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09 Oct

SPS-DSI (DEGAS) Webinar: A Journey Through Graphs for Spatiotemporal Analysis

Date: 9 October 2024
Time: 2:00 PM (Paris Time)
Presenter(s): Jhony H. Giraldo

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22 May

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SPS-DSI (DEGAS) Webinar: Low Distortion Embedding with Bottom-up Manifold Learning

Date: 22 May 2024
Time: 5:00 PM (Paris Time)
Presenter(s): Dr. Gal Mishne

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25 Apr

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SPS-DSI (DEGAS) Webinar: Edge Varying Graph Neural Networks and Their Stability

Date: 25 April 2024
Time: 3:00 PM (Paris Time)
Presenter(s): Elvin Isufi

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13 Mar

SPS-DSI (DEGAS) Webinar: Regularity of Graph Neural Networks

Date: 13 March 2024
Time: 1:00 PM (Paris Time)
Presenter(s): Ron Levie

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IEEE SPS-DSI Webinar: 19 May 2022, by Dr. Usman A. Khan

May 2022
Education & Resources

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SPS DSI Webinar
In many emerging applications, it is of paramount interest to learn hidden parameters from data. For example, self-driving cars may use onboard cameras to identify pedestrians, highway lanes, or traffic signs in various light and weather conditions.
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IEEE SPS-DSI Webinar: 19 October 2022, by Dr. Panagiotis A. Traganitis and Dr. Georgios B. Giannakis

October 2022
Education & Resources

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SPS DSI Webinar

Crowdsourcing has emerged as a powerful paradigm for tackling various machine learning, data mining, and data science tasks, by enlisting inexpensive crowds of human workers, or annotators, to accomplish learning and inference tasks.

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