Neural network, Pattern recognition, Learning, Statistical classification, Machine learning, Computer vision

Differentiable Weighted Finite-State Transducers

On Oct 10, 2020
@syhw shared
RT @awnihannun: We released GTN, a framework for automatic differentiation with weighted automata. Preprint with some example algorithms: https://t.co/ajaWaKlQb3 Code: https://t.co/5QkN5uMgO5 Short blog post: https://t.co/WetE2AxBFc https://t.co/Kr4MK5qY5C
Open

We introduce a framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time. Through the separation of graphs from operations on graphs, this framework enables the exploration of new structured loss functions ...

arxiv.org
On Oct 10, 2020
@syhw shared
RT @awnihannun: We released GTN, a framework for automatic differentiation with weighted automata. Preprint with some example algorithms: https://t.co/ajaWaKlQb3 Code: https://t.co/5QkN5uMgO5 Short blog post: https://t.co/WetE2AxBFc https://t.co/Kr4MK5qY5C
Open

Differentiable Weighted Finite-State Transducers

Differentiable Weighted Finite-State Transducers

We introduce a framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time. Through the separation of graphs ...

A new open source framework for automatic differentiation with graphs

A new open source framework for automatic differentiation with graphs

Introducing GTN, an open source framework for automatic differentiation with a powerful, expressive type of graph called weighted finite-state transducers (WFSTs). With…

Click here to read the article

Click here to read the article

ct 20 20 from gtn import * def ASG(emissions, transitions, target): # Compute constrained and normalization graphs: A = intersect(intersect(target, transitions), emissions) Z = ...

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