4.4k citations · 6.2k across the 79 of their papers we have counts for
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Learning Physical Operators using Neural Operators
Vignesh Gopakumar, Ander Gray, Dan Giles +5
Neural operators have emerged as promising surrogate models for solving partial differential equations (PDEs), but struggle to generalise beyond training distributions and are ofte…
Calibrated Physics-Informed Uncertainty Quantification
Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi +5
Simulating complex physical systems is crucial for understanding and predicting phenomena across diverse fields, such as fluid dynamics and heat transfer, as well as plasma physics…
Parameter Efficient Fine-tuning via Explained Variance Adaptation
Fabian Paischer, Lukas Hauzenberger, Thomas Schmied +3
Foundation models (FMs) are pre-trained on large-scale datasets and then fine-tuned for a specific downstream task. The most common fine-tuning method is to update pretrained weigh…
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
Harry Jake Cunningham, Giorgio Giannone, Mingtian Zhang +1
Global convolutions have shown increasing promise as powerful general-purpose sequence models. However, training long convolutions is challenging, and kernel parameterizations must…
Valid Error Bars for Neural Weather Models using Conformal Prediction
Vignesh Gopakumar, Joel Oskarrson, Ander Gray +5
Neural weather models have shown immense potential as inexpensive and accurate alternatives to physics-based models. However, most models trained to perform weather forecasting do…
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth +1
In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on determinist…
Scalable Data Assimilation with Message Passing
Oscar Key, So Takao, Daniel Giles +1
Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed a…
Gaussian Processes on Cellular Complexes
Mathieu Alain, So Takao, Brooks Paige +1
In recent years, there has been considerable interest in developing machine learning models on graphs to account for topological inductive biases. In particular, recent attention h…
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Alexander Norcliffe, Marc Peter Deisenroth
Neural ODEs demonstrate strong performance in generative and time-series modelling. However, training them via the adjoint method is slow compared to discrete models due to the req…
On Combining Expert Demonstrations in Imitation Learning via Optimal Transport
Ilana Sebag, Samuel Cohen, Marc Peter Deisenroth
Imitation learning (IL) seeks to teach agents specific tasks through expert demonstrations. One of the key approaches to IL is to define a distance between agent and expert and to…
Investigating the Edge of Stability Phenomenon in Reinforcement Learning
Rares Iordan, Marc Peter Deisenroth, Mihaela Rosca
Recent progress has been made in understanding optimisation dynamics in neural networks trained with full-batch gradient descent with momentum with the uncovering of the edge of st…
Understanding Deep Generative Models with Generalized Empirical Likelihoods
Suman Ravuri, Mélanie Rey, Shakir Mohamed +1
Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge. It is especially difficult for certain model cl…
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions
Yicheng Luo, Jackie Kay, Edward Grefenstette +1
Offline reinforcement learning (RL) allows for the training of competent agents from offline datasets without any interaction with the environment. Online finetuning of such offlin…
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes
Sean Nassimiha, Peter Dudfield, Jack Kelly +2
Short-term forecasting of solar photovoltaic energy (PV) production is important for powerplant management. Ideally these forecasts are equipped with error bars, so that downstream…
Optimal Transport for Offline Imitation Learning
Yicheng Luo, Zhengyao Jiang, Samuel Cohen +2
With the advent of large datasets, offline reinforcement learning (RL) is a promising framework for learning good decision-making policies without the need to interact with the rea…
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds
Vu Nguyen, Marc Peter Deisenroth, Michael A. Osborne
Many functions have approximately-known upper and/or lower bounds, potentially aiding the modeling of such functions. In this paper, we introduce Gaussian process models for functi…
Learning to Transfer: A Foliated Theory
Janith Petangoda, Marc Peter Deisenroth, Nicholas A. M. Monk
Learning to transfer considers learning solutions to tasks in a such way that relevant knowledge can be transferred from known task solutions to new, related tasks. This is importa…
The Graph Cut Kernel for Ranked Data
Michelangelo Conserva, Marc Peter Deisenroth, K S Sesh Kumar
Many algorithms for ranked data become computationally intractable as the number of objects grows due to the complex geometric structure induced by rankings. An additional challeng…
Cauchy-Schwarz Regularized Autoencoder
Linh Tran, Maja Pantic, Marc Peter Deisenroth
Recent work in unsupervised learning has focused on efficient inference and learning in latent variables models. Training these models by maximizing the evidence (marginal likeliho…
Learning Contact Dynamics using Physically Structured Neural Networks
Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson +1
Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics…
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty
Simon Olofsson, Eduardo S. Schultz, Adel Mhamdi +3
Diverse domains of science and engineering use parameterised mechanistic models. Engineers and scientists can often hypothesise several rival models to explain a specific process o…
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
Daniel Lengyel, Janith Petangoda, Isak Falk +5
We propose an efficient algorithm to visualise symmetries in neural networks. Typically, models are defined with respect to a parameter space, where non-equal parameters can produc…
A Foliated View of Transfer Learning
Janith Petangoda, Nick A. M. Monk, Marc Peter Deisenroth
Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to related tasks. While this has been studied expe…
Probabilistic Active Meta-Learning
Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth
Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics due to the wear and tear. To address this prob…