gtn
Automatic differentiation with weighted finite-state transducers. (by gtn-org)
gtn | TerpreT | |
---|---|---|
2 | 1 | |
112 | 42 | |
0.0% | - | |
1.8 | 10.0 | |
about 2 years ago | almost 7 years ago | |
C++ | Python | |
MIT License | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gtn
Posts with mentions or reviews of gtn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-08.
-
Differentiable Finite State Machines
FB research has their own version of automatic differentiation of WFSTs: https://github.com/gtn-org/gtn
See also https://github.com/facebookresearch/gtn_applications which contains examples of applications such as handwriting recognition and speech recognition.
- Automatic differentiation with weighted finite-state transducers
TerpreT
Posts with mentions or reviews of TerpreT.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-08.
-
Differentiable Finite State Machines
If you're interested in these kinds of things, many years ago we created TerpreT (https://arxiv.org/pdf/1608.04428.pdf and https://github.com/51alg/TerpreT) to look into generic program synthesis problems, using a set of very different techniques (gradient descent, ILP, SMT) on different problem settings (turing machines, boolean circuits, LLVM IR-style basic blocks, and straight assembly).
What are some alternatives?
When comparing gtn and TerpreT you can also consider the following projects:
k2 - FSA/FST algorithms, differentiable, with PyTorch compatibility.
gtn_applications - Applications using the GTN library and code to reproduce experiments in "Differentiable Weighted Finite-State Transducers"