no code implementations • 6 Nov 2024 • Tyler Clark, Mark Towers, Christine Evers, Jonathon Hare
Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance.
1 code implementation • 15 Nov 2022 • Joseph Early, Ying-Jung Deweese, Christine Evers, Sarvapali Ramchurn
Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation.
no code implementations • 5 Sep 2022 • David M. Bossens, Christine Evers
The challenge of language grounding is to fully understand natural language by grounding language in real-world referents.
1 code implementation • 30 May 2022 • Joseph Early, Tom Bewley, Christine Evers, Sarvapali Ramchurn
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards.
1 code implementation • ICLR 2022 • Joseph Early, Christine Evers, Sarvapali Ramchurn
In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag.
1 code implementation • 3 Sep 2019 • Christine Evers, Heinrich Loellmann, Heinrich Mellmann, Alexander Schmidt, Hendrik Barfuss, Patrick Naylor, Walter Kellermann
The aim of the LOCAlization and TrAcking (LOCATA) Challenge is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking.
1 code implementation • 10 Jan 2019 • Constantinos Papayiannis, Christine Evers, Patrick A. Naylor
A representation of acoustic environments is proposed, which is used to train the GANs.
Audio and Speech Processing Sound