no code implementations • 2 Feb 2021 • Kale-ab Tessera, Sara Hooker, Benjamin Rosman
Based upon these findings, we show that gradient flow in sparse networks can be improved by reconsidering aspects of the architecture design and the training regime.
1 code implementation • 6 Nov 2021 • Ibrahim Salihu Yusuf, Kale-ab Tessera, Thomas Tumiel, Zohra Slim, Amine Kerkeni, Sella Nevo, Arnu Pretorius
In this paper, we compare this random sampling approach to more advanced pseudo-absence generation methods, such as environmental profiling and optimal background extent limitation, specifically for predicting desert locust breeding grounds in Africa.
1 code implementation • 31 Mar 2023 • Claude Formanek, Callum Rhys Tilbury, Jonathan Shock, Kale-ab Tessera, Arnu Pretorius
'Reincarnation' in reinforcement learning has been proposed as a formalisation of reusing prior computation from past experiments when training an agent in an environment.
no code implementations • 30 Nov 2023 • Kale-ab Tessera, Callum Rhys Tilbury, Sasha Abramowitz, Ruan de Kock, Omayma Mahjoub, Benjamin Rosman, Sara Hooker, Arnu Pretorius
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long training times.
no code implementations • 13 Dec 2023 • Omayma Mahjoub, Ruan de Kock, Siddarth Singh, Wiem Khlifi, Abidine Vall, Kale-ab Tessera, Arnu Pretorius
Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL).
no code implementations • 13 Dec 2023 • Siddarth Singh, Omayma Mahjoub, Ruan de Kock, Wiem Khlifi, Abidine Vall, Kale-ab Tessera, Arnu Pretorius
Establishing sound experimental standards and rigour is important in any growing field of research.