Ce soir: Paris Machine Learning #2 Season 2: : Learning Causality, Words, the Higgs & more.

 

Tonight's event will be sponsored by ANEO. Here is the lineup for tonight's Paris Machine Learning Meetup: 

• David Lopez-Paz, "Learning to learn causality" remote from Germany.
• Emanuela Boros, "Learning word representations for event extraction from text"
• Balazs Kegl: “Learning to discover: machine learning in high-energy physics and the HiggsML challenge"
• Cédric Coussinet, Une demo de Nomoseed. http://www.nomoseed.com
• Franck Bardol, Donner un sens aux donnees des ONGs.
• Olivier Roberdet: Prizm, The First Learning Music Player (Kickstarter)
  
• Igor Carron, Un survol du Machine Learning (pour débutants)

The abstracts so far:

Emanuela Boros, "Learning word representations for event extraction from text", http://emanuelaboros.ro

Description: We propose a solution for the event extraction task that relies on learning word representations via a neural network. By automatically learning domain-relevant distributed word representations from a domain-specific unlabeled corpus without complex linguistic processing, a supervised classifier is fed with them. With only these word embeddings representing events, the system outperforms previous state-of-the-art event extraction models

Stay tuned to this entry for more abstracts presentations and video of the meetup.

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