Zobrazeno 1 - 10
of 8 025
pro vyhledávání: '"Albert, Y."'
Autor:
Pike, Sean N., Boggs, Steven E., Brewster, Gabriel, Haight, Sophia E., Roberts, Jarred M., Shih, Albert Y., Szornel, Joanna, Tomsick, John A., Zoglauer, Andreas
We present an investigation into the effects of high-energy proton damage on charge trapping in germanium cross-strip detectors, with the goal of accomplishing three important measurements. First, we calibrated and characterized the spectral resoluti
Externí odkaz:
http://arxiv.org/abs/2412.08836
Autor:
Tavallaie, Omid, Thilakarathna, Kanchana, Seneviratne, Suranga, Seneviratne, Aruna, Zomaya, Albert Y.
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Independently Distributed (IID) data. Traditional FL frameworks adopt the
Externí odkaz:
http://arxiv.org/abs/2409.15067
Autor:
Liu, Bo, Zhan, Liming, Feng, Yujie, Lu, Zexin, Xie, Chengqiang, Xue, Lei, Lam, Albert Y. S., Wu, Xiao-Ming
In the realm of task-oriented dialogue systems, a robust intent detection mechanism must effectively handle malformed utterances encountered in real-world scenarios. This study presents a novel fine-tuning framework for large language models (LLMs) a
Externí odkaz:
http://arxiv.org/abs/2409.11114
Autor:
Nazemi, Niousha, Tavallaie, Omid, Chen, Shuaijun, Mandalari, Anna Maria, Thilakarathna, Kanchana, Holz, Ralph, Haddadi, Hamed, Zomaya, Albert Y.
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data and generates a global model on a server by aggregating model updates.
Externí odkaz:
http://arxiv.org/abs/2409.01722
Autor:
Nazemi, Niousha, Tavallaie, Omid, Mandalari, Anna Maria, Haddadi, Hamed, Holz, Ralph, Zomaya, Albert Y.
This paper investigates the impact of internet centralization on DNS provisioning, particularly its effects on vulnerable populations such as the indigenous people of Australia. We analyze the DNS dependencies of Australian government domains that se
Externí odkaz:
http://arxiv.org/abs/2408.12958
Autor:
Feng, Yujie, Liu, Bo, Dong, Xiaoyu, Lu, Zexin, Zhan, Li-Ming, Lam, Albert Y. S., Wu, Xiao-Ming
An ideal dialogue system requires continuous skill acquisition and adaptation to new tasks while retaining prior knowledge. Dialogue State Tracking (DST), vital in these systems, often involves learning new services and confronting catastrophic forge
Externí odkaz:
http://arxiv.org/abs/2408.09846
Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped with CPUs or GPUs. In the context of server-based Federated Learning as
Externí odkaz:
http://arxiv.org/abs/2408.08699
Autor:
Li, Ming-Feng, Ku, Yueh-Feng, Yen, Hong-Xuan, Liu, Chi, Liu, Yu-Lun, Chen, Albert Y. C., Kuo, Cheng-Hao, Sun, Min
Sparse RGBD scene completion is a challenging task especially when considering consistent textures and geometries throughout the entire scene. Different from existing solutions that rely on human-designed text prompts or predefined camera trajectorie
Externí odkaz:
http://arxiv.org/abs/2407.12939
Recent research has demonstrated the feasibility of training efficient intent detectors based on pre-trained language model~(PLM) with limited labeled data. However, deploying these detectors in resource-constrained environments such as mobile device
Externí odkaz:
http://arxiv.org/abs/2407.09943