36 citations · 138 across the 24 of their papers we have counts for
26 papers
AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting
Anna Allen, Wessel P. Bruinsma, Michael Maier-Gerber +3
AI weather models are in the process of revolutionising weather forecasting. While these models have been shown to achieve superior performance to physics-based NWP in forecasting…
Accurate and scalable exchange-correlation with deep learning
Giulia Luise, Chin-Wei Huang, Thijs Vogels +25
Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable…
Convolutional Conditional Neural Processes
Wessel P. Bruinsma
Neural processes are a family of models which use neural networks to directly parametrise a map from data sets to predictions. Directly parametrising this map enables the use of ex…
Translation Equivariant Transformer Neural Processes
Matthew Ashman, Cristiana Diaconu, Junhyuck Kim +5
The effectiveness of neural processes (NPs) in modelling posterior prediction maps -- the mapping from data to posterior predictive distributions -- has significantly improved sinc…
Approximately Equivariant Neural Processes
Matthew Ashman, Cristiana Diaconu, Adrian Weller +2
Equivariant deep learning architectures exploit symmetries in learning problems to improve the sample efficiency of neural-network-based models and their ability to generalise. How…
Noise-Aware Differentially Private Regression via Meta-Learning
Ossi Räisä, Stratis Markou, Matthew Ashman +4
Many high-stakes applications require machine learning models that protect user privacy and provide well-calibrated, accurate predictions. While Differential Privacy (DP) is the go…
A Foundation Model for the Earth System
Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic +15
Reliable forecasts of the Earth system are crucial for human progress and safety from natural disasters. Artificial intelligence offers substantial potential to improve prediction…
Aardvark weather: end-to-end data-driven weather forecasting
Anna Vaughan, Stratis Markou, Will Tebbutt +8
Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning model…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
Autoregressive Conditional Neural Processes
Wessel P. Bruinsma, Stratis Markou, James Requiema +6
Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning models which produce well-calibrated predictions and are trainable via a simple maximum like…
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt +1
The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel…
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou +8
Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the infor…
Challenges and Pitfalls of Bayesian Unlearning
Ambrish Rawat, James Requeima, Wessel Bruinsma +1
Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model. Approximate unlearning are one class of methods…
A Note on the Chernoff Bound for Random Variables in the Unit Interval
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt
The Chernoff bound is a well-known tool for obtaining a high probability bound on the expectation of a Bernoulli random variable in terms of its sample average. This bound is commo…
Modelling Non-Smooth Signals with Complex Spectral Structure
Wessel P. Bruinsma, Martin Tegnér, Richard E. Turner
The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a model for signals with complex spectral structure. A significant limitation of the GPCM is that it assumes a…
Practical Conditional Neural Processes Via Tractable Dependent Predictions
Stratis Markou, James Requeima, Wessel P. Bruinsma +2
Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and natur…
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Beau Coker, Wessel P. Bruinsma, David R. Burt +2
Bayesian neural networks (BNNs) combine the expressive power of deep learning with the advantages of Bayesian formalism. In recent years, the analysis of wide, deep BNNs has provid…
Efficient Gaussian Neural Processes for Regression
Stratis Markou, James Requeima, Wessel Bruinsma +1
Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attractive family of meta-learning models which produce well-calibrated predictions, enable fast inference at test t…
How Tight Can PAC-Bayes be in the Small Data Regime?
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt +1
In this paper, we investigate the question: Given a small number of datapoints, for example N = 30, how tight can PAC-Bayes and test set bounds be made? For such small datasets, te…
The Gaussian Neural Process
Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong +2
Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes. We provide a rigorous a…
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon +3
Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data. They enable the statistical symmetry of…
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models
Pavel Berkovich, Eric Perim, Wessel Bruinsma
A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved la…
Scalable Exact Inference in Multi-Output Gaussian Processes
Wessel P. Bruinsma, Eric Perim, Will Tebbutt +3
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-te…
Convolutional Conditional Neural Processes
Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong +3
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data. Translation equivar…