@inproceedings{chheda-etal-2021-box,
title = "Box Embeddings: An open-source library for representation learning using geometric structures",
author = "Chheda, Tejas and
Goyal, Purujit and
Tran, Trang and
Patel, Dhruvesh and
Boratko, Michael and
Dasgupta, Shib Sankar and
McCallum, Andrew",
editor = "Adel, Heike and
Shi, Shuming",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.24",
doi = "10.18653/v1/2021.emnlp-demo.24",
pages = "203--211",
abstract = "A fundamental component to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with more geometric structure (eg. distributions, complex or hyperbolic vectors, or regions such as cones, disks, or boxes) have been explored for their alternative inductive biases and additional representational capacity. In this work, we introduce Box Embeddings, a Python library that enables researchers to easily apply and extend probabilistic box embeddings. Fundamental geometric operations on boxes are implemented in a numerically stable way, as are modern approaches to training boxes which mitigate gradient sparsity. The library is fully open source, and compatible with both PyTorch and TensorFlow, which allows existing neural network layers to be replaced with or transformed into boxes easily. In this work, we present the implementation details of the fundamental components of the library, and the concepts required to use box representations alongside existing neural network architectures.",
}
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%0 Conference Proceedings
%T Box Embeddings: An open-source library for representation learning using geometric structures
%A Chheda, Tejas
%A Goyal, Purujit
%A Tran, Trang
%A Patel, Dhruvesh
%A Boratko, Michael
%A Dasgupta, Shib Sankar
%A McCallum, Andrew
%Y Adel, Heike
%Y Shi, Shuming
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F chheda-etal-2021-box
%X A fundamental component to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with more geometric structure (eg. distributions, complex or hyperbolic vectors, or regions such as cones, disks, or boxes) have been explored for their alternative inductive biases and additional representational capacity. In this work, we introduce Box Embeddings, a Python library that enables researchers to easily apply and extend probabilistic box embeddings. Fundamental geometric operations on boxes are implemented in a numerically stable way, as are modern approaches to training boxes which mitigate gradient sparsity. The library is fully open source, and compatible with both PyTorch and TensorFlow, which allows existing neural network layers to be replaced with or transformed into boxes easily. In this work, we present the implementation details of the fundamental components of the library, and the concepts required to use box representations alongside existing neural network architectures.
%R 10.18653/v1/2021.emnlp-demo.24
%U https://aclanthology.org/2021.emnlp-demo.24
%U https://doi.org/10.18653/v1/2021.emnlp-demo.24
%P 203-211
Markdown (Informal)
[Box Embeddings: An open-source library for representation learning using geometric structures](https://aclanthology.org/2021.emnlp-demo.24) (Chheda et al., EMNLP 2021)
ACL