corpus.blog
41554 blogs · [ { "id": "01a0d50c-8d4b-7324-9b4b-b9cc102c10cd", "title": "Offline Meta-Reinforcement Learning in Piecewise Stationary Environments", "url": "https://aidanscannell.com/publications/offline-meta-rl-in-piecewise-stationary-environments/", "published_at": "2026-08-01T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51b73d7e63", "title": "Benchmarking Language Agents on Open-Ended Multi-Agent Coordination in Game Worlds", "url": "https://aidanscannell.com/publications/benchmarking-language-agents-on-open-ended-multi-agent/", "published_at": "2026-06-06T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51b762cadd", "title": "Contextual Latent World Models for Offline Meta Reinforcement Learning", "url": "https://aidanscannell.com/publications/self-predictive-contextual-offline-meta-rl/", "published_at": "2026-03-03T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51b77c3744", "title": "Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking", "url": "https://aidanscannell.com/publications/kla/", "published_at": "2026-02-11T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51b7ea534b", "title": "Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data", "url": "https://aidanscannell.com/publications/efficient-rl-by-guiding-generalist-world-models-with-non-curated-data/", "published_at": "2026-01-25T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51b8595997", "title": "Winning the 1X World Model Challenge", "url": "https://aidanscannell.com/talks/huawei-ai-applications-workshop-dublin-2025/", "published_at": "2025-11-26T11:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51b87d18ff", "title": "Beyond Mamba SSMs: Parallel Kalman Filters as Scalable Primitives for Language Modelling", "url": "https://aidanscannell.com/publications/beyond-mamba-ssms/", "published_at": "2025-11-12T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51b8d8471a", "title": "Forgetting is Everywhere", "url": "https://aidanscannell.com/publications/forgetting-is-everywhere/", "published_at": "2025-11-10T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51b957f18d", "title": "Generative World Modelling for Humanoids: 1X World Model Challenge", "url": "https://aidanscannell.com/talks/iccv-2025-1x-world-model-challenge/", "published_at": "2025-10-19T11:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51b9ce4e7e", "title": "Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report", "url": "https://aidanscannell.com/publications/generative-world-modelling-for-humanoids/", "published_at": "2025-10-10T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51ba581663", "title": "1X World Model Challenge", "url": "https://aidanscannell.com/projects/1x-world-model-challenge/", "published_at": "2025-10-03T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51bb343463", "title": "Huawei-Edinburgh Joint Lab: Discrete Codebook World Models", "url": "https://aidanscannell.com/talks/huawei-edinburgh-joint-lab/", "published_at": "2025-04-02T13:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51bbb8608d", "title": "Generalist World Model Pre-Training for Efficient Reinforcement Learning", "url": "https://aidanscannell.com/publications/generalist-world-model-pretraining/", "published_at": "2025-02-19T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51bc445a27", "title": "Discrete Codebook World Models for Continuous Control", "url": "https://aidanscannell.com/publications/discrete-codebook-world-models/", "published_at": "2025-01-28T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51bd1c3a25", "title": "Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning", "url": "https://aidanscannell.com/publications/entropy-regularized-task-representation-learning/", "published_at": "2025-01-20T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51bd2b7e85", "title": "Discrete Codebook World Models", "url": "https://aidanscannell.com/projects/discrete-codebook-world-models/", "published_at": "2024-10-26T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51bd4522e8", "title": "Nordic AI Meet & AI Day: Sample-efficient Reinforcement Learning with Implicitly Quantized Representations", "url": "https://aidanscannell.com/talks/ai-day-2024-dcwm/", "published_at": "2024-10-21T13:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51be103d39", "title": "iQRL: Implicitly Quantized Representations for Sample-Efficient Reinforcement Learning", "url": "https://aidanscannell.com/talks/iwials-2024/", "published_at": "2024-07-23T12:50:14+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51be98a6d9", "title": "Model-Based Reinforcement Learning", "url": "https://aidanscannell.com/talks/ellis-pml-summer-school-2024/", "published_at": "2024-07-17T11:30:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51befb8227", "title": "iQRL - Implicitly Quantized Representations for Sample-efficient Reinforcement Learning", "url": "https://aidanscannell.com/publications/iqrl/", "published_at": "2024-06-05T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51bf1bf0eb", "title": "Quantized Representations Prevent Dimensional Collapse in Self-predictive RL", "url": "https://aidanscannell.com/publications/arlet-workshop/", "published_at": "2024-06-04T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51bf490b12", "title": "Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning", "url": "https://aidanscannell.com/publications/residual-learning-and-context-encoding-for-adaptive-offline-to-online-rl/", "published_at": "2024-05-20T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c020d7f9", "title": "Function-space Parameterization of Neural Networks for Sequential Learning", "url": "https://aidanscannell.com/publications/function-space-parameterization-of-neural-networks-for-sequential-learning/", "published_at": "2024-05-07T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c04b09fc", "title": "Implicitly Quantized Representations for Reinforcement Learning", "url": "https://aidanscannell.com/projects/quantized-representations-for-rl/", "published_at": "2024-03-24T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c073dd18", "title": "Function-Space Bayesian Deep Learning for Sequential Learning", "url": "https://aidanscannell.com/projects/sfr/", "published_at": "2024-01-10T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c094a403", "title": "Experience", "url": "https://aidanscannell.com/experience/", "published_at": "2023-10-24T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c175dcd3", "title": "(Function-space) Laplace Approximation for Bayesian Neural Networks", "url": "https://aidanscannell.com/talks/safe-rl-talk/", "published_at": "2023-10-03T16:30:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c24a0d5b", "title": "Neural Networks as Sparse Gaussian Processes for Sequential Learning", "url": "https://aidanscannell.com/talks/iwials-2023/", "published_at": "2023-08-15T12:50:14+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c24f9085", "title": "Sparse Function-space Representation of Neural Networks", "url": "https://aidanscannell.com/publications/sparse-function-space-representation-of-neural-networks/", "published_at": "2023-07-21T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c33682eb", "title": "Mode-constrained Model-based Reinforcement Learning via Gaussian Processes", "url": "https://aidanscannell.com/publications/mode-constrained-mbrl/", "published_at": "2023-04-27T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c35305d8", "title": "Model-based reinforcement learning under uncertainty", "url": "https://aidanscannell.com/talks/cambridge-mbrl-under-uncertainty-talk/", "published_at": "2023-02-13T11:30:05+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c3c46429", "title": "Investigating Bayesian Neural Network Dynamics Models for Model-Based Reinforcement Learning", "url": "https://aidanscannell.com/projects/investigating-bnn-dynamics-models-for-mbrl/", "published_at": "2022-11-19T13:44:57+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c3d6bdc4", "title": "Model-based reinforcement learning under uncertainty: the importance of knowing what you don't know", "url": "https://aidanscannell.com/talks/aalto-rl-guest-lecture-mbrl-under-uncertainty/", "published_at": "2022-11-15T12:15:14+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c3e45538", "title": "Mode-Constrained Exploration for Model-Based Reinforcement Learning", "url": "https://aidanscannell.com/projects/mode-constrained-model-based-reinforcement-learning/", "published_at": "2022-09-24T20:40:09+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c4b21e12", "title": "PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems", "url": "https://aidanscannell.com/publications/phd-thesis/", "published_at": "2022-09-24T20:02:01+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c5aaaf14", "title": "Aalto RL Reading Club: Simplifying Model-Based RL: Learning Representations, Latent-Space Models, and Policies with One Objective", "url": "https://aidanscannell.com/talks/aalto-rl-reading-club-simplifying-model-based-rl/", "published_at": "2022-09-23T08:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c63e00ed", "title": "PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems", "url": "https://aidanscannell.com/talks/phd-talk-at-aalto/", "published_at": "2022-09-13T08:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c735fdc0", "title": "Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation", "url": "https://aidanscannell.com/talks/trajectory-optimisation-in-learned-multimodal-dynamical-systems-icra-2021/", "published_at": "2021-06-03T10:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c7bc42ca", "title": "Identifiable Mixtures of Sparse Variational Gaussian Process Experts", "url": "https://aidanscannell.com/publications/identifiable-mixtures-of-sparse-variational-gaussian-process-experts/", "published_at": "2021-05-26T09:48:12+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c7fbe86f", "title": "One Keyboard to Rule Them All - I Built a Dactyl Manuform", "url": "https://aidanscannell.com/post/one-keyboard-to-rule-them-all/", "published_at": "2021-05-09T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c8d857fd", "title": "GPJax - Gaussian Processes in Jax", "url": "https://aidanscannell.com/projects/gaussian-processes-in-jax/", "published_at": "2021-05-06T13:35:24+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c8ffaec9", "title": "Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation", "url": "https://aidanscannell.com/publications/trajectory-optimisation-in-learned-multimodal-dynamical-systems-via-latent-ode-collocation/", "published_at": "2021-03-22T10:57:17+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c90f3b57", "title": "Synergising Bayesian Inference and Probabilistic Geometries for Robotic Control", "url": "https://aidanscannell.com/talks/dtu-geometry-talk-march-2021/", "published_at": "2021-03-18T09:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51c94e96ad", "title": "Creating a CV/Resume in Org-Mode using LaTeX Templates", "url": "https://aidanscannell.com/post/org-mode-resume/", "published_at": "2020-12-01T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51ca46e0ac", "title": "Trajectory Optimisation in Learned Multimodal Dynamical Systems", "url": "https://aidanscannell.com/projects/trajectory-optimisation-in-learned-multimodal-dynamical-systems/", "published_at": "2020-11-16T21:29:53+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51cb166593", "title": "Identifiable Mixtures of Sparse Variational Gaussian Process Experts", "url": "https://aidanscannell.com/projects/mixtures-of-sparse-variational-gaussian-process-experts/", "published_at": "2020-11-16T19:31:03+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51cb9d8d2e", "title": "Setting Up an Emacs Playground on MacOS - Emacs Mac Port | Chemacs | Emacsclient | Spacemacs", "url": "https://aidanscannell.com/post/setting-up-an-emacs-playground-on-mac/", "published_at": "2020-03-29T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51cc0b3927", "title": "How RSI Made Me a Better Developer", "url": "https://aidanscannell.com/post/how-rsi-made-me-a-better-developer/", "published_at": "2020-01-01T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51cc7ef9c9", "title": "Gaussian Process Regression", "url": "https://aidanscannell.com/post/gaussian-process-regression/", "published_at": "2019-10-01T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51cc868c6b", "title": "Welcome to Emacs Anonymous, Sorry, My Blog", "url": "https://aidanscannell.com/post/welcome-to-emacs-anonymous-sorry-my-blog/", "published_at": "2019-09-16T11:14:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51cc94977d", "title": "Cluster log in with SSH", "url": "https://aidanscannell.com/notes/hpc-cluster/hpc_cluster_login/", "published_at": "2019-05-05T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51ccfc5e6b", "title": "Conda environments", "url": "https://aidanscannell.com/notes/hpc-cluster/env/", "published_at": "2019-05-05T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51cd9e2f21", "title": "Debugging on a cluster", "url": "https://aidanscannell.com/notes/hpc-cluster/debugging_on_cluster/", "published_at": "2019-05-05T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51ce53d250", "title": "Hydra submitit launcher", "url": "https://aidanscannell.com/notes/hpc-cluster/hydra/", "published_at": "2019-05-05T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51cf3beec1", "title": "Python", "url": "https://aidanscannell.com/notes/programming/python/", "published_at": "2019-05-05T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d02f7542", "title": "Running Jupyter Notebooks on GPU Clusters", "url": "https://aidanscannell.com/notes/hpc-cluster/jupyter_notebooks_on_cluster/", "published_at": "2019-05-05T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d0d755fa", "title": "Approximate Inference", "url": "https://aidanscannell.com/projects/approximate-inference/", "published_at": "2019-03-13T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d0fdecb0", "title": "Model-Based Reinforcement Learning with Gaussian Processes", "url": "https://aidanscannell.com/projects/pilco/", "published_at": "2019-03-13T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d1c04d8a", "title": "Probabilistic Modelling", "url": "https://aidanscannell.com/projects/probabilistic-modelling/", "published_at": "2019-03-13T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d2b15d40", "title": "Uncertain Agentspeak", "url": "https://aidanscannell.com/projects/uncertain-agentspeak/", "published_at": "2018-09-20T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d32511cc", "title": "Amazon Picking Challenge", "url": "https://aidanscannell.com/projects/amazon-picking-challenge/", "published_at": "2018-04-26T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d420e3de", "title": "Autonomous Vehicle Lane Detection Software", "url": "https://aidanscannell.com/projects/lane-detection/", "published_at": "2017-01-07T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d44fe3b9", "title": "Kidnapped Robot", "url": "https://aidanscannell.com/projects/kidnapped-robot/", "published_at": "2016-12-29T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d531220d", "title": "Ultrasonic Non-Destructive Testing", "url": "https://aidanscannell.com/projects/ultrasonic-ndt/", "published_at": "2016-12-29T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d5f7f020", "title": "UAV Swarm", "url": "https://aidanscannell.com/projects/uav-swarm/", "published_at": "2016-11-10T00:00:00+00:00" }, { "id": "01a08721-862b-7076-b13e-fb51d64fd3cc", "title": "Literate Dotfiles", "url": "https://aidanscannell.com/notes/configs/osx-dotfiles/", "published_at": null }, { "id": "01a08721-862b-7076-b13e-fb51d73e5f4d", "title": "Literate Emacs Configuration", "url": "https://aidanscannell.com/notes/configs/doom-emacs-config/", "published_at": null } ] posts Claim your blog
Back to aidanscannell.com
Blog · corpus.blog/blogs/aidanscannell.com/posts

aidanscannell.com

aidanscannell.com

2026

Offline Meta-Reinforcement Learning in Piecewise Stationary Environmentsoriginal ↗
1 Aug 2026
Benchmarking Language Agents on Open-Ended Multi-Agent Coordination in Game Worldsoriginal ↗
6 Jun 2026
Contextual Latent World Models for Offline Meta Reinforcement Learningoriginal ↗
3 Mar 2026
Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Trackingoriginal ↗
11 Feb 2026
Efficient Reinforcement Learning by Guiding World Models with Non-Curated Dataoriginal ↗
25 Jan 2026

2025

Winning the 1X World Model Challengeoriginal ↗
26 Nov 2025
Beyond Mamba SSMs: Parallel Kalman Filters as Scalable Primitives for Language Modellingoriginal ↗
12 Nov 2025
Forgetting is Everywhereoriginal ↗
10 Nov 2025
Generative World Modelling for Humanoids: 1X World Model Challengeoriginal ↗
19 Oct 2025
Generative World Modelling for Humanoids: 1X World Model Challenge Technical Reportoriginal ↗
10 Oct 2025
1X World Model Challengeoriginal ↗
3 Oct 2025
Huawei-Edinburgh Joint Lab: Discrete Codebook World Modelsoriginal ↗
2 Apr 2025
Generalist World Model Pre-Training for Efficient Reinforcement Learningoriginal ↗
19 Feb 2025
Discrete Codebook World Models for Continuous Controloriginal ↗
28 Jan 2025
Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learningoriginal ↗
20 Jan 2025

2024

Discrete Codebook World Modelsoriginal ↗
26 Oct 2024
Nordic AI Meet & AI Day: Sample-efficient Reinforcement Learning with Implicitly Quantized Representationsoriginal ↗
21 Oct 2024
iQRL: Implicitly Quantized Representations for Sample-Efficient Reinforcement Learningoriginal ↗
23 Jul 2024
Model-Based Reinforcement Learningoriginal ↗
17 Jul 2024
iQRL - Implicitly Quantized Representations for Sample-efficient Reinforcement Learningoriginal ↗
5 Jun 2024
Quantized Representations Prevent Dimensional Collapse in Self-predictive RLoriginal ↗
4 Jun 2024
Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learningoriginal ↗
20 May 2024
Function-space Parameterization of Neural Networks for Sequential Learningoriginal ↗
7 May 2024
Implicitly Quantized Representations for Reinforcement Learningoriginal ↗
24 Mar 2024
Function-Space Bayesian Deep Learning for Sequential Learningoriginal ↗
10 Jan 2024

2023

Experienceoriginal ↗
24 Oct 2023
(Function-space) Laplace Approximation for Bayesian Neural Networksoriginal ↗
3 Oct 2023
Neural Networks as Sparse Gaussian Processes for Sequential Learningoriginal ↗
15 Aug 2023
Sparse Function-space Representation of Neural Networksoriginal ↗
21 Jul 2023
Mode-constrained Model-based Reinforcement Learning via Gaussian Processesoriginal ↗
27 Apr 2023
Model-based reinforcement learning under uncertaintyoriginal ↗
13 Feb 2023

2022

Investigating Bayesian Neural Network Dynamics Models for Model-Based Reinforcement Learningoriginal ↗
19 Nov 2022
Model-based reinforcement learning under uncertainty: the importance of knowing what you don't knoworiginal ↗
15 Nov 2022
Mode-Constrained Exploration for Model-Based Reinforcement Learningoriginal ↗
24 Sept 2022
PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systemsoriginal ↗
24 Sept 2022
Aalto RL Reading Club: Simplifying Model-Based RL: Learning Representations, Latent-Space Models, and Policies with One Objectiveoriginal ↗
23 Sept 2022
PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systemsoriginal ↗
13 Sept 2022

2021

Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocationoriginal ↗
3 Jun 2021
Identifiable Mixtures of Sparse Variational Gaussian Process Expertsoriginal ↗
26 May 2021
One Keyboard to Rule Them All - I Built a Dactyl Manuformoriginal ↗
9 May 2021
GPJax - Gaussian Processes in Jaxoriginal ↗
6 May 2021
Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocationoriginal ↗
22 Mar 2021
Synergising Bayesian Inference and Probabilistic Geometries for Robotic Controloriginal ↗
18 Mar 2021

2020

Creating a CV/Resume in Org-Mode using LaTeX Templatesoriginal ↗
1 Dec 2020
Trajectory Optimisation in Learned Multimodal Dynamical Systemsoriginal ↗
16 Nov 2020
Identifiable Mixtures of Sparse Variational Gaussian Process Expertsoriginal ↗
16 Nov 2020
Setting Up an Emacs Playground on MacOS - Emacs Mac Port | Chemacs | Emacsclient | Spacemacsoriginal ↗
29 Mar 2020
How RSI Made Me a Better Developeroriginal ↗
1 Jan 2020

2019

Gaussian Process Regressionoriginal ↗
1 Oct 2019
Welcome to Emacs Anonymous, Sorry, My Blogoriginal ↗
16 Sept 2019
Cluster log in with SSHoriginal ↗
5 May 2019
Conda environmentsoriginal ↗
5 May 2019
Debugging on a clusteroriginal ↗
5 May 2019
Hydra submitit launcheroriginal ↗
5 May 2019
Pythonoriginal ↗
5 May 2019
Running Jupyter Notebooks on GPU Clustersoriginal ↗
5 May 2019
Approximate Inferenceoriginal ↗
13 Mar 2019
Model-Based Reinforcement Learning with Gaussian Processesoriginal ↗
13 Mar 2019
Probabilistic Modellingoriginal ↗
13 Mar 2019

2018

Uncertain Agentspeakoriginal ↗
20 Sept 2018
Amazon Picking Challengeoriginal ↗
26 Apr 2018

2017

Autonomous Vehicle Lane Detection Softwareoriginal ↗
7 Jan 2017

2016

Kidnapped Robotoriginal ↗
29 Dec 2016
Ultrasonic Non-Destructive Testingoriginal ↗
29 Dec 2016
UAV Swarmoriginal ↗
10 Nov 2016

Undated

Literate Dotfilesoriginal ↗
—
Literate Emacs Configurationoriginal ↗
—