308 citations · 321 across the 6 of their papers we have counts for
6 papers
Learning Layer-wise Equivariances Automatically using Gradients
Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
Convolutions encode equivariance symmetries into neural networks leading to better generalisation performance. However, symmetries provide fixed hard constraints on the functions a…
Current Methods for Drug Property Prediction in the Real World
Jacob Green, Cecilia Cabrera Diaz, Maximilian A. H. Jakobs +3
Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials, and to find highly active compounds faster. Interest from the M…
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk +2
Selecting hyperparameters in deep learning greatly impacts its effectiveness but requires manual effort and expertise. Recent works show that Bayesian model selection with Laplace…
Actually Sparse Variational Gaussian Processes
Harry Jake Cunningham, Daniel Augusto de Souza, So Takao +2
Gaussian processes (GPs) are typically criticised for their unfavourable scaling in both computational and memory requirements. For large datasets, sparse GPs reduce these demands…
Memory Safe Computations with XLA Compiler
Artem Artemev, Tilman Roeder, Mark van der Wilk
Software packages like TensorFlow and PyTorch are designed to support linear algebra operations, and their speed and usability determine their success. However, by prioritising spe…
GPflow: A Gaussian process library using TensorFlow
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson +5
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational…