EDRep 0.1.1

Efficient distributed representations with linear-time Softmax normalization

EDRep

Dall'Amico, Belliardo - Learning distributed representations with efficient SoftMax normalization

| Documentation | Paper |

pip install git+https://github.com/lorenzodallamico/EDRep.git

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This is the Python implementation of the work presented in (Dall'Amico, Belliardo - Learning distributed representations with efficient SoftMax normalization). We propose a 2Vec-like algorithm, formulated for as a general purpose embedding problem. We consider a set of $n$ objects for which we want to obtain a distributed representation $X \in \mathbb{R}^{n\times d}$ in a $d$-dimensional Euclidean space. The algorithm requires a probability matrix $P \in \mathbb{R}^{n\times n}$ as input whose entries $P_{ij}$ are a measure of affinity between the objects $i$ and $j$. We then train the embedding vectors to approximate the input distribution described by the matrix $P$ with a variational approach based on the use of the softmax function of $XX^T$ to learn the embedding, in the spirit of 2Vec algorithms.

In (Dall'Amico, Belliardo - Learning distributed representations with efficient SoftMax normalization) we described an efficient way to estimate in $\mathcal{O}(n)$ operations the normalization constants of ${\rm SoftMax}(XX^T)$ and hence to optimize the efficiently the proposed loss function.

In our implementation, we also consider the case in which $P$ is a rectangular matrix $P\in \mathbb{R}^{n\times m}$ and two embedding matrices need to be learned: $X \in \mathbb{R}^{n\times d}, Y \in \mathbb{R}^{m\times d}$. We refer the reader to the paper and to the documentation for further details on this use case.

We refer the user to the paper and to the documentation for further details.

Requirements

We also shared an Anaconda environment in which all codes were run and tested. You can create it by running the following commands in the terminal

conda env create -f EDRep_env.yml
conda activate EDRep

Citation

If you make use of these codes, please use the following citation:

@article{
dall'amico2025learning,
title={Learning distributed representations with efficient SoftMax normalization},
author={Lorenzo Dall'Amico and Enrico Maria Belliardo},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2025},
url={https://openreview.net/forum?id=9M4NKMZOPu},
note={}
}

Authors

• Lorenzo Dall'Amico - [e-mail]
• Enrico Maria Belliardo - [e-mail]

License

This software is released under the GNU AFFERO GENERAL PUBLIC LICENSE (see included file LICENSE)