SynJax: Structured Probability Distributions for JAX
The development of deep learning software libraries enabled significant
progress in the field by allowing users to focus on modeling, while letting the
library to take care of the tedious and time-consuming task of optimizing
execution for modern hardware accelerators. However, this has benefited only
particular types of deep learning models, such as Transformers, whose
primitives map easily to the vectorized computation. The models that explicitly
account for structured objects, such as trees and segmentations, did not
benefit equally because they require custom algorithms that are difficult to
implement in a vectorized form.
  SynJax directly addresses this problem by providing an efficient vectorized
implementation of inference algorithms for structured distributions covering
alignment, tagging, segmentation, constituency trees and spanning trees. With
SynJax we can build large-scale differentiable models that explicitly model
structure in the data. The code is available at
https://github.com/deepmind/synjax.
64d1d93a749587dbe0665961
Miloš Stanojević
claimed_verified
2023-08-09T00:42:48.379Z
64d2626b07abea8697becb14
/avatars/475fb745858efda6eb6b54c60d6ac79e.svg
Milos Stanojevic
user
stanojevic
64d1d93a749587dbe0665962
Laurent Sartran
SynJax: Structured Probability Distributions for JAX
The development of deep learning software libraries enabled significant
progress in the field by allowing users to focus on modeling, while letting the
library to take care of the tedious and time-consuming task of optimizing
execution for modern hardware accelerators. However, this has benefited only
particular types of deep learning models, such as Transformers, whose
primitives map easily to the vectorized computation. The models that explicitly
account for structured objects, such as trees and segmentations, did not
benefit equally because they require custom algorithms that are difficult to
implement in a vectorized form.
  SynJax directly addresses this problem by providing an efficient vectorized
implementation of inference algorithms for structured distributions covering
alignment, tagging, segmentation, constituency trees and spanning trees. With
SynJax we can build large-scale differentiable models that explicitly model
structure in the data. The code is available at
https://github.com/deepmind/synjax.
SynJax provides efficient vectorized inference for structured distributions, enabling large-scale differentiable models that account for structured data, like trees and segmentations.
vectorized computation Transformers structured distributions inference algorithms alignment tagging segmentation constituency trees spanning trees differentiable models