Machine-Learning

Huawei-Edinburgh Joint Lab: Discrete Codebook World Models

Nordic AI Meet & AI Day: Sample-efficient Reinforcement Learning with Implicitly Quantized Representations

iQRL: Implicitly Quantized Representations for Sample-Efficient Reinforcement Learning

I will be presenting our research on self-supervised representation learning for reinforcement learning at the [International Workshop of Intelligent Autonomous Learning Systems …

Model-Based Reinforcement Learning

I'll be giving a lecture on model-based RL at the Cambridge Ellis Unit Summer School on Probabilistic Machine Learning 2024.

Function-space Parameterization of Neural Networks for Sequential Learning

Aidan Scannell

• 7 May, 2024 • 1 min read

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Implicitly Quantized Representations for Reinforcement Learning

Learning representations for reinforcement learning (RL) has shown much promise for continuous control. In this project, we investigate using vector quantization to prevent …

Function-Space Bayesian Deep Learning for Sequential Learning

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes …

(Function-space) Laplace Approximation for Bayesian Neural Networks

Neural Networks as Sparse Gaussian Processes for Sequential Learning

I will be presenting our research on bayesian deep learning for sequential learning at the [International Workshop of Intelligent Autonomous Learning Systems …

Sparse Function-space Representation of Neural Networks

Aidan Scannell

• 21 Jul, 2023 • 1 min read

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