Hyperbolic CNNs outperform Euclidean CNNs on 37 of 42 genome classification benchmarks and beat several large DNA language models on 7 GUE tasks using orders of magnitude fewer parameters.
Manify: A Python Library for Learning Non-Euclidean Representations
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abstract
We present Manify, an open-source Python library for non-Euclidean representation learning. Leveraging manifold learning techniques, Manify provides tools for learning embeddings in (products of) non-Euclidean spaces, performing classification and regression with data that lives in such spaces, estimating the curvature of a manifold, and more. Manify aims to advance research and applications in machine learning by offering a comprehensive suite of tools for manifold-based data analysis. Our source code, examples, and documentation are available at https://github.com/pchlenski/manify.
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Hyperbolic Genome Embeddings
Hyperbolic CNNs outperform Euclidean CNNs on 37 of 42 genome classification benchmarks and beat several large DNA language models on 7 GUE tasks using orders of magnitude fewer parameters.