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Interpretable Deep Gaussian Processes for Geospatial Tasks
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Thin and Deep Gaussian Processes
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, …
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Thin and Deep Gaussian Processes
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions
This paper presents a novel Learning from Demonstration (LfD) method that uses neural fields to learn new skills efficiently and …
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Sliding Touch-based Exploration for Modeling Unknown Object Shape with Multi-finger Hands
Efficient and accurate 3D object shape reconstruction contributes significantly to the success of a robot’s physical interaction …
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Safe Trajectory Sampling in Model-based Reinforcement Learning
Model-based reinforcement learning aims to learn a policy to solve a target task by leveraging a learned dynamics model. This approach, …
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Safe Trajectory Sampling in Model-based Reinforcement Learning
Understanding Deep Generative Models with Generalized Empirical Likelihoods
Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge. It …
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Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and …
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Deep Grasp Adaptation through Domain Transfer
Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and …
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Optimal Transport for Offline Imitation Learning
With the advent of large datasets, offline reinforcement learning is a promising framework for learning good decision-making policies …
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Actually Sparse Variational Gaussian Processes
A typical criticism of Gaussian processes is their unfavourable scaling in both compute and memory requirements. Sparse variational …
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