6 papers
Scaling few-shot spoken word classification with generative meta-continual learning
Louise Beyers, Batsirayi Mupamhi Ziki, Ruan van der Merwe
Few-shot spoken word classification has largely been developed for applications where a small number of classes is considered, and so the potential of larger-scale few-shot spoken…
Does language matter for spoken word classification? A multilingual generative meta-learning approach
Batsirayi Mupamhi Ziki, Louise Beyers, Ruan van der Merwe
Meta-learning has been shown to have better performance than supervised learning for few-shot monolingual spoken word classification. However, the meta-learning approach remains un…
Sable: a Performant, Efficient and Scalable Sequence Model for MARL
Omayma Mahjoub, Sasha Abramowitz, Ruan de Kock +8
As multi-agent reinforcement learning (MARL) progresses towards solving larger and more complex problems, it becomes increasingly important that algorithms exhibit the key properti…
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation
Claude Formanek, Callum Rhys Tilbury, Louise Beyers +2
Offline multi-agent reinforcement learning (MARL) is an emerging field with great promise for real-world applications. Unfortunately, the current state of research in offline MARL…
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning
Claude Formanek, Louise Beyers, Callum Rhys Tilbury +2
Offline multi-agent reinforcement learning (MARL) is an exciting direction of research that uses static datasets to find optimal control policies for multi-agent systems. Though th…
Coordination Failure in Cooperative Offline MARL
Callum Rhys Tilbury, Claude Formanek, Louise Beyers +2
Offline multi-agent reinforcement learning (MARL) leverages static datasets of experience to learn optimal multi-agent control. However, learning from static data presents several…