Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Lee, Royson"'
Autor:
Chen, Hao Mark, Tan, Fuwen, Kouris, Alexandros, Lee, Royson, Fan, Hongxiang, Venieris, Stylianos I.
In spite of the great potential of large language models (LLMs) across various tasks, their deployment on resource-constrained devices remains challenging due to their excessive computational and memory demands. Quantization has emerged as an effecti
Externí odkaz:
http://arxiv.org/abs/2410.13461
Autor:
Tan, Fuwen, Lee, Royson, Dudziak, Łukasz, Hu, Shell Xu, Bhattacharya, Sourav, Hospedales, Timothy, Tzimiropoulos, Georgios, Martinez, Brais
Large language models (LLMs) have revolutionized language processing, delivering outstanding results across multiple applications. However, deploying LLMs on edge devices poses several challenges with respect to memory, energy, and compute costs, lim
Externí odkaz:
http://arxiv.org/abs/2408.13933
Autor:
Lee, Royson, Fernandez-Marques, Javier, Hu, Shell Xu, Li, Da, Laskaridis, Stefanos, Dudziak, Łukasz, Hospedales, Timothy, Huszár, Ferenc, Lane, Nicholas D.
Federated learning (FL) has enabled distributed learning of a model across multiple clients in a privacy-preserving manner. One of the main challenges of FL is to accommodate clients with varying hardware capacities; clients have differing compute an
Externí odkaz:
http://arxiv.org/abs/2405.14791
Autor:
Lee, Royson, Kim, Minyoung, Li, Da, Qiu, Xinchi, Hospedales, Timothy, Huszár, Ferenc, Lane, Nicholas D.
Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those common models to the specifics of each client's local data distribution. H
Externí odkaz:
http://arxiv.org/abs/2310.02420
In recent years, image and video delivery systems have begun integrating deep learning super-resolution (SR) approaches, leveraging their unprecedented visual enhancement capabilities while reducing reliance on networking conditions. Nevertheless, de
Externí odkaz:
http://arxiv.org/abs/2212.09501
Autor:
Lee, Royson, Li, Rui, Venieris, Stylianos I., Hospedales, Timothy, Huszár, Ferenc, Lane, Nicholas D.
Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images. Among these methods, explicit kernel estimation approaches have demonstrated unprecedented performance at han
Externí odkaz:
http://arxiv.org/abs/2212.07886
Autor:
Fan, Hongxiang, Chau, Thomas, Venieris, Stylianos I., Lee, Royson, Kouris, Alexandros, Luk, Wayne, Lane, Nicholas D., Abdelfattah, Mohamed S.
Attention-based neural networks have become pervasive in many AI tasks. Despite their excellent algorithmic performance, the use of the attention mechanism and feed-forward network (FFN) demands excessive computational and memory resources, which oft
Externí odkaz:
http://arxiv.org/abs/2209.09570
Deep learning-based blind super-resolution (SR) methods have recently achieved unprecedented performance in upscaling frames with unknown degradation. These models are able to accurately estimate the unknown downscaling kernel from a given low-resolu
Externí odkaz:
http://arxiv.org/abs/2108.08305
Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360{\deg} videos to video-conferencing and live streaming. However, robustly delivering visual content under fluctuating
Externí odkaz:
http://arxiv.org/abs/2106.03727
Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360-degree videos to video-conferencing and live streaming. However, robustly delivering visual content under fluctuatin
Externí odkaz:
http://arxiv.org/abs/2010.05838