Search Results for author:
Found 17 papers, 8 papers with code
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
To address this difficulty, we present GeometricKernels, a software package which implements the geometric analogs of classical Euclidean squared exponential - also known as heat - and Mat\'ern kernels, which are widely-used in settings where uncertainty is of key interest.
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform
In this work, we unify conditional training and sampling using the mathematically well-understood Doob's h-transform.
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design
Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision.
Geometric Neural Diffusion Processes
In this work, we extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling.
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations.
Memory-Based Meta-Learning on Non-Stationary Distributions
Memory-based meta-learning is a technique for approximating Bayes-optimal predictors.
Neural Diffusion Processes
Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference.
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy.
GPflux: A Library for Deep Gaussian Processes
GPflux is compatible with and built on top of the Keras deep learning eco-system.
A Tutorial on Sparse Gaussian Processes and Variational Inference
In this context, a convenient choice for approximate inference is variational inference (VI), where the problem of Bayesian inference is cast as an optimization problem -- namely, to maximize a lower bound of the log marginal likelihood.
Sparse Gaussian Processes with Spherical Harmonic Features
We introduce a new class of inter-domain variational Gaussian processes (GP) where data is mapped onto the unit hypersphere in order to use spherical harmonic representations.
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Many machine learning models require a training procedure based on running stochastic gradient descent.
Scalable Thompson Sampling using Sparse Gaussian Process Models
We provide theoretical guarantees and show that the drastic reduction in computational complexity of scalable TS can be enjoyed without loss in the regret performance over the standard TS.
A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for small variations in the model or inference.
Deep Gaussian Processes with Importance-Weighted Variational Inference
Deep Gaussian processes (DGPs) can model complex marginal densities as well as complex mappings.
Bayesian Image Classification with Deep Convolutional Gaussian Processes
We also demonstrate that our fully Bayesian approach improves on dropout-based Bayesian deep learning methods in terms of uncertainty and marginal likelihood estimates.
Gaussian Process Conditional Density Estimation
Conditional Density Estimation (CDE) models deal with estimating conditional distributions.
