Zobrazeno 1 - 10
of 7 233
pro vyhledávání: '"de Kock, A."'
Autor:
Daniel, Jemma, de Kock, Ruan, Nessir, Louay Ben, Abramowitz, Sasha, Mahjoub, Omayma, Khlifi, Wiem, Formanek, Claude, Pretorius, Arnu
The Transformer model has demonstrated success across a wide range of domains, including in Multi-Agent Reinforcement Learning (MARL) where the Multi-Agent Transformer (MAT) has emerged as a leading algorithm in the field. However, a significant draw
Externí odkaz:
http://arxiv.org/abs/2410.19382
Autor:
Mahjoub, Omayma, Abramowitz, Sasha, de Kock, Ruan, Khlifi, Wiem, Toit, Simon du, Daniel, Jemma, Nessir, Louay Ben, Beyers, Louise, Formanek, Claude, Clark, Liam, Pretorius, Arnu
As the field of multi-agent reinforcement learning (MARL) progresses towards larger and more complex environments, achieving strong performance while maintaining memory efficiency and scalability to many agents becomes increasingly important. Althoug
Externí odkaz:
http://arxiv.org/abs/2410.01706
Autor:
de Kock, Christine
In-group language is an important signifier of group dynamics. This paper proposes a novel method for inducing lexicons of in-group language, which incorporates its socio-temporal context. Existing methods for lexicon induction do not capture the evo
Externí odkaz:
http://arxiv.org/abs/2409.19257
Autor:
de Kock, Christine
Group interactions take place within a particular socio-temporal context, which should be taken into account when modelling communities. We propose a method for jointly modelling community structure and language over time, and apply it in the context
Externí odkaz:
http://arxiv.org/abs/2409.19243
Autor:
Liu, Zhiwei, Yang, Kailai, Xie, Qianqian, de Kock, Christine, Ananiadou, Sophia, Hovy, Eduard
Misinformation is prevalent in various fields such as education, politics, health, etc., causing significant harm to society. However, current methods for cross-domain misinformation detection rely on time and resources consuming fine-tuning and comp
Externí odkaz:
http://arxiv.org/abs/2406.11093
Autor:
Giammanco, Andrea, Moussawi, Marwa Al, Boone, Matthieu, De Kock, Tim, De Roy, Judy, Huysmans, Sam, Kumar, Vishal, Lagrange, Maxime, Tytgat, Michael
In cultural heritage conservation, it is increasingly common to rely on non-destructive imaging methods based on the absorption or scattering of photons ($X$ or $\gamma$ rays) or neutrons. However, physical and practical issues limit these techniques
Externí odkaz:
http://arxiv.org/abs/2405.10417
Autor:
Ousidhoum, Nedjma, Muhammad, Shamsuddeen Hassan, Abdalla, Mohamed, Abdulmumin, Idris, Ahmad, Ibrahim Said, Ahuja, Sanchit, Aji, Alham Fikri, Araujo, Vladimir, Beloucif, Meriem, De Kock, Christine, Hourrane, Oumaima, Shrivastava, Manish, Solorio, Thamar, Surange, Nirmal, Vishnubhotla, Krishnapriya, Yimam, Seid Muhie, Mohammad, Saif M.
We present the first shared task on Semantic Textual Relatedness (STR). While earlier shared tasks primarily focused on semantic similarity, we instead investigate the broader phenomenon of semantic relatedness across 14 languages: Afrikaans, Algeria
Externí odkaz:
http://arxiv.org/abs/2403.18933
Autor:
Ousidhoum, Nedjma, Muhammad, Shamsuddeen Hassan, Abdalla, Mohamed, Abdulmumin, Idris, Ahmad, Ibrahim Said, Ahuja, Sanchit, Aji, Alham Fikri, Araujo, Vladimir, Ayele, Abinew Ali, Baswani, Pavan, Beloucif, Meriem, Biemann, Chris, Bourhim, Sofia, De Kock, Christine, Dekebo, Genet Shanko, Hourrane, Oumaima, Kanumolu, Gopichand, Madasu, Lokesh, Rutunda, Samuel, Shrivastava, Manish, Solorio, Thamar, Surange, Nirmal, Tilaye, Hailegnaw Getaneh, Vishnubhotla, Krishnapriya, Winata, Genta, Yimam, Seid Muhie, Mohammad, Saif M.
Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language con
Externí odkaz:
http://arxiv.org/abs/2402.08638
Autor:
Khlifi, Wiem, Singh, Siddarth, Mahjoub, Omayma, de Kock, Ruan, Vall, Abidine, Gorsane, Rihab, Pretorius, Arnu
Cooperative multi-agent reinforcement learning (MARL) has made substantial strides in addressing the distributed decision-making challenges. However, as multi-agent systems grow in complexity, gaining a comprehensive understanding of their behaviour
Externí odkaz:
http://arxiv.org/abs/2312.08468
Autor:
Mahjoub, Omayma, de Kock, Ruan, Singh, Siddarth, Khlifi, Wiem, Vall, Abidine, Tessera, Kale-ab, Pretorius, Arnu
Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL). In cooperative MARL, team performance is typically inferred from a single shared global reward. Arguably, among the best current
Externí odkaz:
http://arxiv.org/abs/2312.08466