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Semantic Cross-Pose Correspondence from a Single Example
This article focuses on predicting how an object can be transformed to a semantically meaningful pose relative to another object, given …
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How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making?
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Learning Chaos In A Linear Way
Learning long-term behaviors in chaotic dynamical systems, such as turbulent flows and climate modelling, is challenging due to their …
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Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current …
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Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
Global convolutions have shown increasing promise as powerful general-purpose sequence models. However, training long convolutions is …
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Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
Streaming Bayes GFlowNets
Bayes’ rule naturally allows for inference refinement in a streaming fashion, without the need to recompute posteriors from scratch …
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Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials
Unlike traditional rigid robots, soft robots offer more flexibility, compliance, and adaptability. They are also typically cheaper to …
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Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials
Gaussian Processes on Cellular Complexes
In recent years, there has been considerable interest in developing machine learning models on graphs to account for topological …
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Gaussian Processes on Cellular Complexes
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems
Data assimilation (DA) methods use priors arising from differential equations to robustly interpolate and extrapolate data. Popular …
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A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for …
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A Unifying Variational Framework for Gaussian Process Motion Planning