Zobrazeno 1 - 10
of 8 288
pro vyhledávání: '"Nguyễn Văn Thật"'
Autor:
Nguyen, Van Thang
Typical deep neural video compression networks usually follow the hybrid approach of classical video coding that contains two separate modules: motion coding and residual coding. In addition, a symmetric auto-encoder is often used as a normal archite
Externí odkaz:
http://arxiv.org/abs/2411.17160
Autor:
Chen, Fengchao, Wu, Tingmin, Nguyen, Van, Wang, Shuo, Hu, Hongsheng, Abuadbba, Alsharif, Rudolph, Carsten
Phishing remains a pervasive cyber threat, as attackers craft deceptive emails to lure victims into revealing sensitive information. While Artificial Intelligence (AI), particularly deep learning, has become a key component in defending against phish
Externí odkaz:
http://arxiv.org/abs/2411.11389
Autor:
Nguyen, Nhan Thanh, Nguyen, Van-Dinh, Nguyen, Hieu V., Ngo, Hien Quoc, Swindlehurst, A. Lee, Juntti, Markku
Integrated sensing and communications (ISAC) is envisioned as a key feature in future wireless communications networks. Its integration with massive multiple-input-multiple-output (MIMO) techniques promises to leverage substantial spatial beamforming
Externí odkaz:
http://arxiv.org/abs/2411.10723
Internet of Things and Deep Learning are synergetically and exponentially growing industrial fields with a massive call for their unification into a common framework called Edge AI. While on-device inference is a well-explored topic in recent researc
Externí odkaz:
http://arxiv.org/abs/2411.06346
Autor:
Nguyen, Lan-Huong, Nguyen, Van-Linh, Hwang, Ren-Hung, Kuo, Jian-Jhih, Chen, Yu-Wen, Huang, Chien-Chung, Pan, Ping-I
Many nations are promoting the green transition in the energy sector to attain neutral carbon emissions by 2050. Smart Grid 2.0 (SG2) is expected to explore data-driven analytics and enhance communication technologies to improve the efficiency and su
Externí odkaz:
http://arxiv.org/abs/2411.04365
Mixture of Experts (MoEs) plays an important role in the development of more efficient and effective large language models (LLMs). Due to the enormous resource requirements, studying large scale MoE algorithms remain in-accessible to many researchers
Externí odkaz:
http://arxiv.org/abs/2411.00918
Autor:
Chen, Junan, Ronchetti, Matteo, Stehl, Verena, Nguyen, Van, Kallaa, Muhannad Al, Gedara, Mahesh Thalwaththe, Lölkes, Claudia, Moser, Stefan, Seidl, Maximilian, Wieczorek, Matthias
Recent developments in the registration of histology and micro-computed tomography ({\mu}CT) have broadened the perspective of pathological applications such as virtual histology based on {\mu}CT. This topic remains challenging because of the low ima
Externí odkaz:
http://arxiv.org/abs/2410.14343
Autor:
Nguyen, Van Tuan, Beuran, Razvan
This paper proposes a novel federated learning approach for improving IoT network intrusion detection. The rise of IoT has expanded the cyber attack surface, making traditional centralized machine learning methods insufficient due to concerns about d
Externí odkaz:
http://arxiv.org/abs/2410.14121
Autor:
Salmond, Daniel, Nguyen, Van, Uzunov, Anton V., Nikolova, Natalia, Desai, Prajakta, Kyprianou, Ross
Our thesis is that operating in cyberspace is challenging because cyberspace exhibits extreme variety, high malleability, and extreme velocity. These properties make cyberspace largely inscrutable and limits one's agency in cyberspace, where agency i
Externí odkaz:
http://arxiv.org/abs/2410.13076
Traditional molecule generation methods often rely on sequence or graph-based representations, which can limit their expressive power or require complex permutation-equivariant architectures. This paper introduces a novel paradigm for learning molecu
Externí odkaz:
http://arxiv.org/abs/2410.12522