Zobrazeno 1 - 10
of 490
pro vyhledávání: '"Halgamuge Saman"'
Autor:
Wu, Haihang, Wang, Wei, Malepathirana, Tamasha, Seneviratne, Sachith, Oetomo, Denny, Halgamuge, Saman
Pruning can be an effective method of compressing large pre-trained models for inference speed acceleration. Previous pruning approaches rely on access to the original training dataset for both pruning and subsequent fine-tuning. However, access to t
Externí odkaz:
http://arxiv.org/abs/2412.07114
Adapting Large Language Models (LLMs) that are extensively trained on abundant text data, and customizing the input prompt to enable time series forecasting has received considerable attention. While recent work has shown great potential for adapting
Externí odkaz:
http://arxiv.org/abs/2412.04806
Accurate forecasts of distributed solar generation are necessary to reduce negative impacts resulting from the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time interval
Externí odkaz:
http://arxiv.org/abs/2411.10921
Neural networks are powerful function approximators, yet their ``black-box" nature often renders them opaque and difficult to interpret. While many post-hoc explanation methods exist, they typically fail to capture the underlying reasoning processes
Externí odkaz:
http://arxiv.org/abs/2408.14780
Cost optimization is a common goal of workflow schedulers operating in cloud computing environments. The use of spot instances is a potential means of achieving this goal, as they are offered by cloud providers at discounted prices compared to their
Externí odkaz:
http://arxiv.org/abs/2408.02926
Deep Reinforcement Learning (DRL) techniques have been successfully applied for solving complex decision-making and control tasks in multiple fields including robotics, autonomous driving, healthcare and natural language processing. The ability of DR
Externí odkaz:
http://arxiv.org/abs/2408.02938
Autor:
Chen, Ken, Seneviratne, Sachith, Wang, Wei, Hu, Dongting, Saha, Sanjay, Hasan, Md. Tarek, Rasnayaka, Sanka, Malepathirana, Tamasha, Gong, Mingming, Halgamuge, Saman
Animating stylized avatars with dynamic poses and expressions has attracted increasing attention for its broad range of applications. Previous research has made significant progress by training controllable generative models to synthesize animations
Externí odkaz:
http://arxiv.org/abs/2406.13272
Autor:
Perera, Rashindrie, Halgamuge, Saman
In this paper, we look at cross-domain few-shot classification which presents the challenging task of learning new classes in previously unseen domains with few labelled examples. Existing methods, though somewhat effective, encounter several limitat
Externí odkaz:
http://arxiv.org/abs/2403.04492
Regional solar power forecasting, which involves predicting the total power generation from all rooftop photovoltaic systems in a region holds significant importance for various stakeholders in the energy sector. However, the vast amount of solar pow
Externí odkaz:
http://arxiv.org/abs/2403.01653
Autor:
Wu, Haihang, Wang, Wei, Malepathirana, Tamasha, Senanayake, Damith, Oetomo, Denny, Halgamuge, Saman
Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks. One crucial aspect of neural growth is determining the optimal growth timing. However, few stu
Externí odkaz:
http://arxiv.org/abs/2401.03104