Box Embeddings: An open-source library for representation learning using geometric structures
This work provides a tool for researchers to explore alternative inductive biases and representational capacities in geometric structures, but it is incremental as it builds on existing concepts without introducing new methods.
The authors tackled the problem of applying and extending probabilistic box embeddings by introducing an open-source Python library, Box Embeddings, which facilitates representation learning using geometric structures.
A major factor contributing to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with geometric structures (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 capacities. In this work, we introduce Box Embeddings, a Python library that enables researchers to easily apply and extend probabilistic box embeddings.