Learning to communicate through imagination with model-based deep multi-agent reinforcement learning

no code implementations • 1 Jan 2021 • Arnu Pretorius, Scott Cameron, Andries Petrus Smit, Elan van Biljon, Lawrence Francis, Femi Azeez, Alexandre Laterre, Karim Beguir

Furthermore, the core utility of our imagination is deeply coupled with communication.

Multi-agent Reinforcement Learning reinforcement-learning +1

A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning

1 code implementation • NeurIPS 2020 • Arnu Pretorius, Scott Cameron, Elan van Biljon, Tom Makkink, Shahil Mawjee, Jeremy du Plessis, Jonathan Shock, Alexandre Laterre, Karim Beguir

Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control.

Management Multi-agent Reinforcement Learning +2

Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages

4 code implementations • Findings of the Association for Computational Linguistics 2020 • Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, Timi Fasubaa, Tajudeen Kolawole, Taiwo Fagbohungbe, Solomon Oluwole Akinola, Shamsuddeen Hassan Muhammad, Salomon Kabongo, Salomey Osei, Sackey Freshia, Rubungo Andre Niyongabo, Ricky Macharm, Perez Ogayo, Orevaoghene Ahia, Musie Meressa, Mofe Adeyemi, Masabata Mokgesi-Selinga, Lawrence Okegbemi, Laura Jane Martinus, Kolawole Tajudeen, Kevin Degila, Kelechi Ogueji, Kathleen Siminyu, Julia Kreutzer, Jason Webster, Jamiil Toure Ali, Jade Abbott, Iroro Orife, Ignatius Ezeani, Idris Abdulkabir Dangana, Herman Kamper, Hady Elsahar, Goodness Duru, Ghollah Kioko, Espoir Murhabazi, Elan van Biljon, Daniel Whitenack, Christopher Onyefuluchi, Chris Emezue, Bonaventure Dossou, Blessing Sibanda, Blessing Itoro Bassey, Ayodele Olabiyi, Arshath Ramkilowan, Alp Öktem, Adewale Akinfaderin, Abdallah Bashir

Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved.

Machine Translation Translation

On Optimal Transformer Depth for Low-Resource Language Translation

1 code implementation • 9 Apr 2020 • Elan van Biljon, Arnu Pretorius, Julia Kreutzer

Therefore, by showing that transformer models perform well (and often best) at low-to-moderate depth, we hope to convince fellow researchers to devote less computational resources, as well as time, to exploring overly large models during the development of these systems.

Machine Translation NMT +1

Masakhane -- Machine Translation For Africa

2 code implementations • 13 Mar 2020 • Iroro Orife, Julia Kreutzer, Blessing Sibanda, Daniel Whitenack, Kathleen Siminyu, Laura Martinus, Jamiil Toure Ali, Jade Abbott, Vukosi Marivate, Salomon Kabongo, Musie Meressa, Espoir Murhabazi, Orevaoghene Ahia, Elan van Biljon, Arshath Ramkilowan, Adewale Akinfaderin, Alp Öktem, Wole Akin, Ghollah Kioko, Kevin Degila, Herman Kamper, Bonaventure Dossou, Chris Emezue, Kelechi Ogueji, Abdallah Bashir

Africa has over 2000 languages.

Machine Translation Translation

If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks

no code implementations • 13 Oct 2019 • Arnu Pretorius, Elan van Biljon, Benjamin van Niekerk, Ryan Eloff, Matthew Reynard, Steve James, Benjamin Rosman, Herman Kamper, Steve Kroon

Our results therefore suggest that, in the shallow-to-moderate depth setting, critical initialisation provides zero performance gains when compared to off-critical initialisations and that searching for off-critical initialisations that might improve training speed or generalisation, is likely to be a fruitless endeavour.

Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks

no code implementations • 16 Apr 2019 • Ryan Eloff, André Nortje, Benjamin van Niekerk, Avashna Govender, Leanne Nortje, Arnu Pretorius, Elan van Biljon, Ewald van der Westhuizen, Lisa van Staden, Herman Kamper

For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis.

Acoustic Unit Discovery Speech Synthesis

Critical initialisation for deep signal propagation in noisy rectifier neural networks

1 code implementation • NeurIPS 2018 • Arnu Pretorius, Elan van Biljon, Steve Kroon, Herman Kamper

Simulations and experiments on real-world data confirm that our proposed initialisation is able to stably propagate signals in deep networks, while using an initialisation disregarding noise fails to do so.