Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Rosman Benjamin"'
Autor:
Nigatu, Hellina Hailu, Tonja, Atnafu Lambebo, Rosman, Benjamin, Solorio, Thamar, Choudhury, Monojit
The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a `low-resource language.
Externí odkaz:
http://arxiv.org/abs/2410.20817
A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason about new situations by combining aspects of previous experiences. These models leverage compositional architectures wh
Externí odkaz:
http://arxiv.org/abs/2409.14981
Autor:
Tonja, Atnafu Lambebo, Dossou, Bonaventure F. P., Ojo, Jessica, Rajab, Jenalea, Thior, Fadel, Wairagala, Eric Peter, Aremu, Anuoluwapo, Moiloa, Pelonomi, Abbott, Jade, Marivate, Vukosi, Rosman, Benjamin
High-resource language models often fall short in the African context, where there is a critical need for models that are efficient, accessible, and locally relevant, even amidst significant computing and data constraints. This paper introduces Inkub
Externí odkaz:
http://arxiv.org/abs/2408.17024
Reinforcement learning (RL) has progressed substantially over the past decade, with much of this progress being driven by benchmarks. Many benchmarks are focused on video or board games, and a large number of robotics benchmarks lack diversity and re
Externí odkaz:
http://arxiv.org/abs/2407.14516
Autor:
Moodley, Perusha, Kaushik, Pramod, Thambi, Dhillu, Trovinger, Mark, Paruchuri, Praveen, Hong, Xia, Rosman, Benjamin
Decision Transformers, in their vanilla form, struggle to perform on image-based environments with multi-discrete action spaces. Although enhanced Decision Transformer architectures have been developed to improve performance, these methods have not s
Externí odkaz:
http://arxiv.org/abs/2407.01310
Publikováno v:
MATEC Web of Conferences, Vol 388, p 04009 (2023)
Spatial awareness is an important competence for a mobile robotic system. A robot needs to localise and perform context interpretation to provide any meaningful service. With the deep learning tools and readily available sensors, visual place recogni
Externí odkaz:
https://doaj.org/article/9b26009bfd7c4257984c2ff73b1725c9
Autor:
Crafford Gerrie, Rosman Benjamin
Publikováno v:
MATEC Web of Conferences, Vol 370, p 07008 (2022)
Different reinforcement learning (RL) methods exist to address the problem of combining multiple different learners to generate a superior learner. These existing methods usually assume that each learner uses the same algorithm and/or state represent
Externí odkaz:
https://doaj.org/article/8337818de6db4000ac7e77e72fabafff
Autor:
Bester, Tristan, Rosman, Benjamin
Financial inclusion ensures that individuals have access to financial products and services that meet their needs. As a key contributing factor to economic growth and investment opportunity, financial inclusion increases consumer spending and consequ
Externí odkaz:
http://arxiv.org/abs/2402.11066
We present counting reward automata-a finite state machine variant capable of modelling any reward function expressible as a formal language. Unlike previous approaches, which are limited to the expression of tasks as regular languages, our framework
Externí odkaz:
http://arxiv.org/abs/2312.11364
Autor:
Tessera, Kale-ab, Tilbury, Callum Rhys, Abramowitz, Sasha, de Kock, Ruan, Mahjoub, Omayma, Rosman, Benjamin, Hooker, Sara, Pretorius, Arnu
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long training times. To address this difficulty, we propose the framework of Generalisable Agents for Neural Network Optimisa
Externí odkaz:
http://arxiv.org/abs/2311.18598