Aidan Scannell
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    • Discrete Codebook World Models
    • Implicitly Quantized Representations for Reinforcement Learning
    • Function-Space Bayesian Deep Learning for Sequential Learning
    • Investigating Bayesian Neural Network Dynamics Models for Model-Based Reinforcement Learning
    • Mode-Constrained Exploration for Model-Based Reinforcement Learning
    • GPJax - Gaussian Processes in Jax
    • Trajectory Optimisation in Learned Multimodal Dynamical Systems
    • Identifiable Mixtures of Sparse Variational Gaussian Process Experts
    • Approximate Inference
    • Model-Based Reinforcement Learning with Gaussian Processes
    • Probabilistic Modelling
    • Uncertain Agentspeak
    • Amazon Picking Challenge
    • Autonomous Vehicle Lane Detection Software
    • Kidnapped Robot
    • Ultrasonic Non-Destructive Testing
    • UAV Swarm
  • Publications
    • Efficient Reinforcement Learning by Guiding Generalist World Models with Non-Curated Data
    • Generalist World Model Pre-Training for Efficient Reinforcement Learning
    • Discrete Codebook World Models for Continuous Control
    • Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning
    • iQRL - Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
    • Quantized Representations Prevent Dimensional Collapse in Self-predictive RL
    • Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning
    • Function-space Parameterization of Neural Networks for Sequential Learning
    • Sparse Function-space Representation of Neural Networks
    • Mode-constrained Model-based Reinforcement Learning via Gaussian Processes
    • PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems
    • Identifiable Mixtures of Sparse Variational Gaussian Process Experts
    • Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation
    • An example preprint / working paper
    • An example journal article
    • An example conference paper
  • Experience
  • Posts
    • One Keyboard to Rule Them All - I Built a Dactyl Manuform
    • Creating a CV/Resume in Org-Mode using LaTeX Templates
    • Setting Up an Emacs Playground on MacOS - Emacs Mac Port | Chemacs | Emacsclient | Spacemacs
    • How RSI Made Me a Better Developer
    • Gaussian Process Regression
    • Welcome to Emacs Anonymous, Sorry, My Blog
  • Recent & Upcoming Talks
    • 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
    • Model-Based Reinforcement Learning
    • (Function-space) Laplace Approximation for Bayesian Neural Networks
    • Neural Networks as Sparse Gaussian Processes for Sequential Learning
    • Model-based reinforcement learning under uncertainty
    • Model-based reinforcement learning under uncertainty: the importance of knowing what you don't know
    • Aalto RL Reading Club: Simplifying Model-Based RL: Learning Representations, Latent-Space Models, and Policies with One Objective
    • PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems
    • Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation
    • Synergising Bayesian Inference and Probabilistic Geometries for Robotic Control
  • HPC Cluster
    • Cluster log in with SSH
    • Conda environments
    • Debugging on a cluster
    • Hydra submitit launcher
    • Running Jupyter Notebooks on GPU Clusters
  • .Dotfiles
    • Literate Dotfiles
    • Literate Emacs Configuration
  • Programming
    • Python
Notes
.Dotfiles

.Dotfiles

My .dotfiles for OSX, Emacs, etc…

  • Literate Dotfiles
  • Literate Emacs Configuration
Last updated on 3 Oct, 2025

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