Facebook’s Open Source Framework For Training Graph-Based ML Models. Facebook recently open-sourced the graph transformer networks (GTN) framework for effectively training graph-based learning models.
Facebook recently open-sourced the graph transformer networks (GTN) framework for effectively training graph-based learning models. In this case, GTN will be used in automatic differentiation of weighted finite-state transducers (WFSTs), which is an expressive and powerful graph. With GTN, researchers can easily construct WFSTs, visualize them, and perform operations on them
This framework enables the separation of graphs from operations on them that helps in exploring new structured loss functions and which in turn makes the encoding of prior knowledge on learning algorithms easier. Further, in a paper published by Awni Hannun, Vineel Pratap, Jacob Kahn & Wei-Ning Hsu of the Facebook AI Research, in this regard, proposed a convolutional WFST layer to be used in the interior of a deep neural network for mapping lower-level to higher-level representations.
GTN is written in C++ and has bindings to Python. GTN can be used to express and design sequence-level loss functions. With this framework, Facebook aims to make experimentation with structures in learning algorithms much simpler.
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