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Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials
Sicelukwanda Zwane, Daniel Cheney, Curtis C. Johnson +4
Soft robots offer more flexibility, compliance, and adaptability than traditional rigid robots. They are also typically lighter and cheaper to manufacture. However, their use in re…
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…
Uncertainty Quantification of Surrogate Models using Conformal Prediction
Vignesh Gopakumar, Ander Gray, Joel Oskarsson +5
Data-driven surrogate models offer quick approximations to complex numerical and experimental systems but typically lack uncertainty quantification, limiting their reliability in s…
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…
RDM: Recurrent Diffusion Model for Human Motion Generation
Mirgahney Mohamed, Harry Jake Cunningham, Marc P. Deisenroth +2
Human motion generation is a challenging task due to its high dimensionality and the difficulty of generating fine-grained motions. Diffusion methods have been proposed due to thei…
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…
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems
Rafael Anderka, Marc Peter Deisenroth, So Takao
Data assimilation (DA) methods use priors arising from differential equations to robustly interpolate and extrapolate data. Popular techniques such as ensemble methods that handle…