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Graph, Data-science, and Deep Learning

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Roelof Pieters
Roelof Pieters

short few pointers on Graphs for Deep Learning, given at the Deep Learning Meetup Stockholm, 20 April 2015

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Download to read offline
Wadda you mean, Graph?Wadda you mean Graph?
A graph is a graph is a graph
 what drugs will bind to protein X and not interact with drug Y?
Graph-based Deep Learning • Graphs + NLP = AWESOME • Novel possibilities: Time is a fundamental factor for any analysis • Graph (natural NLP and other fancy affinities) vs Matrices (complicated, not scalable algorithms/ mathematics) • Whiteboard/Visualization friendly • Thin Application layer = Focus on the graph, not the software layer
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Chris Manning, Andrew Ng and Chris Potts. 2013. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. EMNLP 2013 code & demo: http://nlp.stanford.edu/sentiment/index.html Graph-based Deep Learning*
Graph-based Deep Learning
Graph-based Deep Learning

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Graph, Data-science, and Deep Learning

  • 1. Wadda you mean, Graph?Wadda you mean Graph?
  • 2.
  • 3.
  • 4.
  • 5. A graph is a graph is a graph
 what drugs will bind to protein X and not interact with drug Y?
  • 6. Graph-based Deep Learning • Graphs + NLP = AWESOME • Novel possibilities: Time is a fundamental factor for any analysis • Graph (natural NLP and other fancy affinities) vs Matrices (complicated, not scalable algorithms/ mathematics) • Whiteboard/Visualization friendly • Thin Application layer = Focus on the graph, not the software layer
  • 7. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Chris Manning, Andrew Ng and Chris Potts. 2013. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. EMNLP 2013 code & demo: http://nlp.stanford.edu/sentiment/index.html Graph-based Deep Learning*