Zobrazeno 1 - 10
of 5 220
pro vyhledávání: '"Kha, A."'
Unsupervised domain adaptation (UDA) has become increasingly prevalent in scene text recognition (STR), especially where training and testing data reside in different domains. The efficacy of existing UDA approaches tends to degrade when there is a l
Externí odkaz:
http://arxiv.org/abs/2410.09913
Fairness in artificial intelligence and machine learning (AI/ML) models is becoming critically important, especially as decisions made by these systems impact diverse groups. In education, a vital sector for all countries, the widespread application
Externí odkaz:
http://arxiv.org/abs/2410.06423
Autor:
Hoang, Manh Kha, Le, Tuan Anh, Thuc, Kieu-Xuan, Van Luyen, Tong, Yang, Xin-She, Ng, Derrick Wing Kwan
This letter addresses a multivariate optimization problem for linear movable antenna arrays (MAAs). Particularly, the position and beamforming vectors of the under-investigated MAA are optimized simultaneously to maximize the minimum beamforming gain
Externí odkaz:
http://arxiv.org/abs/2409.04228
This paper examines integrated satellite-terrestrial networks (ISTNs) in urban environments, where terrestrial networks (TNs) and non-terrestrial networks (NTNs) share the same frequency band in the C-band which is considered the promising band for b
Externí odkaz:
http://arxiv.org/abs/2408.15394
Autor:
Tian, Minyang, Gao, Luyu, Zhang, Shizhuo Dylan, Chen, Xinan, Fan, Cunwei, Guo, Xuefei, Haas, Roland, Ji, Pan, Krongchon, Kittithat, Li, Yao, Liu, Shengyan, Luo, Di, Ma, Yutao, Tong, Hao, Trinh, Kha, Tian, Chenyu, Wang, Zihan, Wu, Bohao, Xiong, Yanyu, Yin, Shengzhu, Zhu, Minhui, Lieret, Kilian, Lu, Yanxin, Liu, Genglin, Du, Yufeng, Tao, Tianhua, Press, Ofir, Callan, Jamie, Huerta, Eliu, Peng, Hao
Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evaluations. We address this issue by examining LMs' capabilities to generat
Externí odkaz:
http://arxiv.org/abs/2407.13168
Autor:
Wang, Danqing, Antoniades, Antonis, Luong, Kha-Dinh, Zhang, Edwin, Kosan, Mert, Li, Jiachen, Singh, Ambuj, Wang, William Yang, Li, Lei
Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations
Externí odkaz:
http://arxiv.org/abs/2406.13869
Autor:
Ding, Ruiwen, Luong, Kha-Dinh, Rodriguez, Erika, da Silva, Ana Cristina Araujo Lemos, Hsu, William
In computational pathology, extracting spatial features from gigapixel whole slide images (WSIs) is a fundamental task, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of this analysis is aggregating in
Externí odkaz:
http://arxiv.org/abs/2406.04377
This paper studies the channel model for the integrated satellite-terrestrial networks operating at C-band under deployment in dense urban and rural areas. Particularly, the interference channel from the low-earth-orbit (LEO) satellite to the dense u
Externí odkaz:
http://arxiv.org/abs/2405.12839
Autor:
Addlesee, Angus, Denley, Daniel, Edmondson, Andy, Gunson, Nancie, Garcia, Daniel Hernández, Kha, Alexandre, Lemon, Oliver, Ndubuisi, James, O'Reilly, Neil, Perochaud, Lia, Valeri, Raphaël, Worika, Miebaka
Conversational agents participating in multi-party interactions face significant challenges in dialogue state tracking, since the identity of the speaker adds significant contextual meaning. It is common to utilise diarisation models to identify the
Externí odkaz:
http://arxiv.org/abs/2311.03021
Autor:
Luong, Kha-Dinh, Singh, Ambuj
Property prediction on molecular graphs is an important application of Graph Neural Networks. Recently, unlabeled molecular data has become abundant, which facilitates the rapid development of self-supervised learning for GNNs in the chemical domain.
Externí odkaz:
http://arxiv.org/abs/2310.03274