Zobrazeno 1 - 10
of 411
pro vyhledávání: '"McCreadie P"'
Financial asset recommendation (FAR) is a sub-domain of recommender systems which identifies useful financial securities for investors, with the expectation that they will invest capital on the recommended assets. FAR solutions analyse and learn from
Externí odkaz:
http://arxiv.org/abs/2407.08692
Recommender systems can be helpful for individuals to make well-informed decisions in complex financial markets. While many studies have focused on predicting stock prices, even advanced models fall short of accurately forecasting them. Additionally,
Externí odkaz:
http://arxiv.org/abs/2404.07223
Autor:
Long, Zijun, Zhuang, Lipeng, Killick, George, McCreadie, Richard, Camarasa, Gerardo Aragon, Henderson, Paul
Human-annotated vision datasets inevitably contain a fraction of human mislabelled examples. While the detrimental effects of such mislabelling on supervised learning are well-researched, their influence on Supervised Contrastive Learning (SCL) remai
Externí odkaz:
http://arxiv.org/abs/2403.06289
Text-to-image retrieval aims to find the relevant images based on a text query, which is important in various use-cases, such as digital libraries, e-commerce, and multimedia databases. Although Multimodal Large Language Models (MLLMs) demonstrate st
Externí odkaz:
http://arxiv.org/abs/2402.15276
Autor:
Long, Zijun, Killick, George, Zhuang, Lipeng, Aragon-Camarasa, Gerardo, Meng, Zaiqiao, Mccreadie, Richard
State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning using Cross-Entropy loss~(CE). However, it has been demonstrated that
Externí odkaz:
http://arxiv.org/abs/2402.14551
Publikováno v:
Proceedings of the 20th International ISCRAM Conference 2023, pp. 309--319
In times of emergency, crisis response agencies need to quickly and accurately assess the situation on the ground in order to deploy relevant services and resources. However, authorities often have to make decisions based on limited information, as d
Externí odkaz:
http://arxiv.org/abs/2401.02838
In recent years, the rapid growth of online multimedia services, such as e-commerce platforms, has necessitated the development of personalised recommendation approaches that can encode diverse content about each item. Indeed, modern multi-modal reco
Externí odkaz:
http://arxiv.org/abs/2310.20343
Robotic vision applications often necessitate a wide range of visual perception tasks, such as object detection, segmentation, and identification. While there have been substantial advances in these individual tasks, integrating specialized models in
Externí odkaz:
http://arxiv.org/abs/2310.10221
As Multimodal Large Language Models (MLLMs) grow in size, adapting them to specialized tasks becomes increasingly challenging due to high computational and memory demands. Indeed, traditional fine-tuning methods are costly, due to the need for extens
Externí odkaz:
http://arxiv.org/abs/2309.01516
State-of-the-art image models predominantly follow a two-stage strategy: pre-training on large datasets and fine-tuning with cross-entropy loss. Many studies have shown that using cross-entropy can result in sub-optimal generalisation and stability.
Externí odkaz:
http://arxiv.org/abs/2308.14893