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Arnu Pretorius
Arnu Pretorius
Staff Research Scientist, InstaDeep Ltd
Email verificata su instadeep.com
Titolo
Citata da
Citata da
Anno
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
R Eloff, A Nortje, B van Niekerk, A Govender, L Nortje, A Pretorius, ...
arXiv preprint arXiv:1904.07556, 2019
632019
Towards a standardised performance evaluation protocol for cooperative marl
R Gorsane, O Mahjoub, RJ de Kock, R Dubb, S Singh, A Pretorius
Advances in Neural Information Processing Systems 35, 5510-5521, 2022
462022
A meta-analysis of research in random forests for classification
A Pretorius, S Bierman, SJ Steel
2016 Pattern Recognition Association of South Africa and Robotics and …, 2016
442016
On optimal transformer depth for low-resource language translation
EV Biljon, A Pretorius, J Kreutzer
The International Conference on Learning Representations (ICLR 2020), 2020
40*2020
Learning dynamics of linear denoising autoencoders
A Pretorius, S Kroon, H Kamper
International Conference on Machine Learning, 2018
372018
Human decision making and artificial intelligence: a comparison in the domain of sports prediction
A Pretorius, DA Parry
Proceedings of the Annual Conference of the South African Institute of …, 2016
322016
Jumanji: a diverse suite of scalable reinforcement learning environments in jax
C Bonnet, D Luo, D Byrne, S Surana, S Abramowitz, P Duckworth, ...
arXiv preprint arXiv:2306.09884, 2023
28*2023
Mava: A research framework for distributed multi-agent reinforcement learning
A Pretorius, K Tessera, AP Smit, C Formanek, SJ Grimbly, K Eloff, ...
arXiv e-prints, arXiv: 2107.01460, 2021
21*2021
Combinatorial optimization with policy adaptation using latent space search
F Chalumeau, S Surana, C Bonnet, N Grinsztajn, A Pretorius, A Laterre, ...
Advances in Neural Information Processing Systems 36, 7947-7959, 2023
192023
Causal multi-agent reinforcement learning: Review and open problems
SJ Grimbly, J Shock, A Pretorius
arXiv preprint arXiv:2111.06721, 2021
182021
Critical initialisation for deep signal propagation in noisy rectifier neural networks
A Pretorius, E Van Biljon, S Kroon, H Kamper
Advances in Neural Information Processing Systems, 5722-5731, 2018
182018
Off-the-grid marl: a framework for dataset generation with baselines for cooperative offline multi-agent reinforcement learning
C Formanek, A Jeewa, J Shock, A Pretorius
arXiv preprint arXiv:2302.00521 9, 2023
152023
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
A Pretorius, S Cameron, E Van Biljon, T Makkink, S Mawjee, J du Plessis, ...
Advances in neural information processing systems 33, 9983-9994, 2020
152020
A bias-variance analysis of ensemble learning for classification
A Pretorius, S Bierman, SJ Steel
Annual proceedings of the south african statistical association conference …, 2016
112016
Scaling multi-agent reinforcement learning to full 11 versus 11 simulated robotic football
A Smit, HA Engelbrecht, W Brink, A Pretorius
Autonomous Agents and Multi-Agent Systems 37 (1), 20, 2023
82023
Universally expressive communication in multi-agent reinforcement learning
M Morris, TD Barrett, A Pretorius
Advances in Neural Information Processing Systems 35, 33508-33522, 2022
82022
On the expected behaviour of noise regularised deep neural networks as Gaussian processes
A Pretorius, H Kamper, S Kroon
Pattern Recognition Letters 138, 75-81, 2020
72020
Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A
A Smit, P Duckworth, N Grinsztajn, K Tessera, TD Barrett, A Pretorius
arXiv preprint arXiv:2311.17371, 2023
52023
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
AP Smit, N Grinsztajn, P Duckworth, TD Barrett, A Pretorius
Forty-first International Conference on Machine Learning, 0
5
Reduce, reuse, recycle: Selective reincarnation in multi-agent reinforcement learning
JC Formanek, CR Tilbury, JP Shock, A Pretorius
Workshop on Reincarnating Reinforcement Learning at ICLR 2023, 2023
42023
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20