Facebook's Open Source Framework For Training Graph-Based ML Models

In this case, GTN will be used in automatic differentiation of weighted finite-state transducers (WFSTs), which is an expressive and powerful graph. 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.