Hyperlib: Deep learning in the Hyperbolic space


Hyperlib: Deep learning in the Hyperbolic space

Hyperlib: Deep learning in the Hyperbolic space

Hyperlib is a cutting-edge python library that makes it easy to create the next generation of neural networks in the hyperbolic space (as opposed to Euclidean).

Hyperbolic geometry is different from Euclidean geometry – It has more capacity which means it can fit a wider range of data. Hyperbolic geometry is particularly suited to data that has an underlying hierarchical structure. There’s also a growing amount of research documenting the benefits of modelling the brain using hyperbolic over Euclidean geometry.

Hyperlib makes hyperbolic neural networks accessible, abstracting away all of the complicated maths. We hope it will inspire further hyperbolic research.

Since the famed ‘Image-net’ moment in 2012 deep learning has become the dominant field within Artificial Intelligence research. Deep learning can be defined as training an Artificial Neural Network that has many layers within it to learn representations of input data that makes it easier to perform a task. A task, in this case, can be anything from classifying whether an image is a photograph of a cat or a dog, to predicting if it will rain in New York tomorrow.

Deep learning models have been able to outperform humans in a number of tasks, as Deepmind demonstrated most recently with their AlphaFold 2 model, but these models are still limited in a number of ways. One limitation is the geometry that the majority of these models work within. Recent works have explored the ways in which deep learning models can be improved by generalising them to alternative geometries…

Source: www.nalex.ai/post/hyperlib-deep-learning-in-the-hyperbolic-space

Code: https://github.com/nalexai/hyperlib

June 2, 2021
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