Matching Structured Data
Online seminar with Romain Tavenard (Université de Rennes, LETG-COSTEL).
Practical information:
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- Register until: 16 Jun 2022
- Price: Free
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Leertraject
The definition of adequate metrics between objects to be compared is
at the core of many machine learning methods (e.g., nearest neighbors,
kernel machines, etc.). When complex objects are involved, such metrics
have to be carefully designed in order to leverage on desired notions of
similarity.
This talk covers my works related to the definition of new metrics for structured data such as time series or graphs.
You can view the overview of the presentation here.
Teacher / speaker
Romain Tavenard
Romain Tavenard graduated from École Centrale de Lyon and ENS Cachan/Rennes, and received his PhD degree from University of Rennes 1 in 2011. After two years as a post-doctoral fellow at Idiap Research Institute, he is now an assistant professor at University of Rennes 2.
Here, prof. Tavenard investigates dedicated machine learning methods for environmental time series.
AI for Time Series
Several research groups in the Flanders AI Research Program conduct world-class research on time series, both in the development of algorithms and tools, as in a wide area of application fields. In a recent poll in the Flanders AI community, ‘time series’ came up as the most wanted topic for future workshops or courses. With this seminar series, we bring together researchers that are interested in, or are conducting research related to, time series. We offer a varied program of national and international speakers.
Klik on the separate webinars below to (re)watch the recordings.
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