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Overlooked Implications of the Reconstruction Loss for VAE Disentanglement
Learning disentangled representations with variational autoencoders (VAEs) is often attributed to the regularisation component of the …
Nathan Michlo
,
Richard Klein
,
Steven James
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Supplementary Material
Augmentative Topology Agents For Open-ended Learning
We tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents while also evolving …
Muhammad Umair Nasir
,
Michael Beukman
,
Steven James
,
Christopher Cleghorn
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On The Specialization of Neural Modules
A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason …
Devon Jarvis
,
Richard Klein
,
Benjamin Rosman
,
Andrew Saxe
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Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases
The ability to generate synthetic data has a variety of use cases across different domains. In education research, there is a growing …
Herkulaas Combrink
,
Vukosi Marivate
,
Benjamin Rosman
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Real Time In-Game Playstyle Classification Using A Hybrid Probabilistic Supervised Learning Approach
In interactive digital media, such as video games, bringing about an adaptive or personalised experience requires a mechanism for …
Lindsay John Arendse
,
Branden Ingram
,
Benjamin Rosman
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Project
Reinforcement Learning in Education: A Multi-Armed Bandit Approach
Advances in reinforcement learning research have demonstrated the ways in which different agent-based models can learn how to optimally …
Herkulaas Combrink
,
Vukosi Marivate
,
Benjamin Rosman
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Improving Reinforcement Learning with Ensembles of Different Learners
Different reinforcement learning (RL) methods exist to address the problem of combining multiple different learners to generate a …
Gerrie Crafford
,
Benjamin Rosman
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Reducing the Planning Horizon through Reinforcement Learning
Planning is a computationally expensive process, which can limit the reactivity of autonomous agents. Planning problems are usually …
Logan Dunbar
,
Benjamin Rosman
,
Anthony G. Cohn
,
Matteo Leonetti
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Improved Action Prediction through Multiple Model Processing of Player Trajectories
Action prediction in video games is the process of extracting useful information in order to predict the future actions of a player. …
Branden Ingram
,
Benjamin Rosman
,
Clint van Alten
,
Richard Klein
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Project
Play-style Identification through Deep Unsupervised Clustering of Trajectories
In any game, play-style is a concept that describes the technique and strategy employed by a player to achieve a goal. Being able to …
Branden Ingram
,
Benjamin Rosman
,
Clint van Alten
,
Richard Klein
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