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Found 7 papers, 5 papers with code
BEND: Benchmarking DNA Language Models on biologically meaningful tasks
The genome sequence contains the blueprint for governing cellular processes.
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters
After the recent ground-breaking advances in protein structure prediction, one of the remaining challenges in protein machine learning is to reliably predict distributions of structural states.
Adaptive Cholesky Gaussian Processes
We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data.
What is a meaningful representation of protein sequences?
How we choose to represent our data has a fundamental impact on our ability to subsequently extract information from them.
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
We contribute to the WGAN literature by introducing the family of $(q, p)$-Wasserstein GANs, which allow the use of more general $p$-Wasserstein metrics for $p\geq 1$ in the GAN learning procedure.
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
We prove that equivariant convolutions are the most general equivariant linear maps between fields over R^3.
Spherical convolutions and their application in molecular modelling
We show that the models are capable of learning non-trivial functions in these molecular environments, and that our spherical convolutions generally outperform standard 3D convolutions in this setting.
