This library contains an implementation for efficient computation of zeroth-, first-, and second-order expectations under spanning tree models. A detailed description of these algorithms including proofs of correctness and rumtime can be found in "Efficient Computation of Expectations under Spanning Tree Distributions".
This code is for the paper Efficient Computation of Expectations under Spanning Tree Distributions. Please cite as:
@inproceedings{zmigrod-etal-2020-efficient,
title = "Efficient Computation of Expectations under Spanning Tree Distributions",
author = "Ran Zmigrod and Tim Vieira and Ryan Cotterell",
journal = "Transactions of the Association for Computational Linguistics",
year = "2020",
url = "https://arxiv.org/abs/2008.12988",
}- Python version >= 3.6
- PyTorch version >= 1.6.0
Installation:
git clone https://github.com/rycolab/tree_expectations
cd tree_expectations
pip install -e .Variable names:
w: input matrix
rho: root weight vector
A: adjacency matrix
q: multiplicatively decomposable function
r, s, f: additively decomposable function
We assume that w, r, s are structured as the root weight vector (rho) along the diagonal and the rest is the adjacency matrix (A).
We give type annotations and use the following dimensions
N = 'number of nodes'
E = 'number of egdes (typically N^2)'
R = 'Dimensionality of r function'
S = 'Dimensionality of s function'
F = 'Dimensionality of f function'