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Skill Machines: Temporal Logic Composition in Reinforcement Learning
A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretable and verifiable. One common …
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Participatory Translations of Oshiwambo: Towards Culture Preservation with Language Technology
In this paper, we describe a participatory, collaborative, and cost-effective process for creating translations in Oshiwambo, the most …
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Augmentative Topology Agents For Open-ended Learning
In this work, we tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents and increasingly …
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Facilitating Safe Sim-to-Real through Simulator Abstraction and Zero-shot Task Composition
Simulators are a fundamental part of training robots to solve complex control and navigation tasks. This is due to the speed and safety …
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Skill Machines: Temporal Logic Composition in Reinforcement Learning
A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretable and verifiable. One common …
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Just-in-Time Sparsity: Learning Dynamic Sparsity Schedules
Sparse neural networks have various computational benefits while often being able to maintain or improve the generalization performance …
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World Value Functions: Knowledge Representation for Learning and Planning
We propose world value functions (WVFs), a type of goaloriented general value function that represents how to solve not just a given …
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Analysing the Effects of Transfer Learning on Low-Resourced Named Entity Recognition Performance
Transfer learning has led to large gains in performance for nearly all NLP tasks while making downstream models easier and faster to …
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Effect of Tokenisation Strategies for Low-Resourced Southern African Languages
Research into machine translation for African languages is very limited and low- resourced in terms of datasets and model evaluations. …
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Generalisation in Lifelong Reinforcement Learning through Logical Composition
We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether …
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