Zobrazeno 1 - 10
of 292 994
pro vyhledávání: '"NGUYEN, P."'
Autor:
Nguyen, Christopher, Nguyen, William, Suzuki, Atsushi, Oku, Daisuke, Phan, Hong An, Dinh, Sang, Nguyen, Zooey, Ha, Anh, Raghavan, Shruti, Vo, Huy, Nguyen, Thang, Nguyen, Lan, Hirayama, Yoshikuni
Large Language Models (LLMs) have demonstrated the potential to address some issues within the semiconductor industry. However, they are often general-purpose models that lack the specialized knowledge needed to tackle the unique challenges of this s
Externí odkaz:
http://arxiv.org/abs/2411.13802
Natural Language Inference (NLI) is a task within Natural Language Processing (NLP) that holds value for various AI applications. However, there have been limited studies on Natural Language Inference in Vietnamese that explore the concept of joint m
Externí odkaz:
http://arxiv.org/abs/2411.13407
Autor:
Nguyen, Hoang-Quan, Nguyen, Xuan-Bac, Churchill, Hugh, Choudhary, Arabinda Kumar, Sinha, Pawan, Khan, Samee U., Luu, Khoa
Vision-brain understanding aims to extract semantic information about brain signals from human perceptions. Existing deep learning methods for vision-brain understanding are usually introduced in a traditional learning paradigm missing the ability to
Externí odkaz:
http://arxiv.org/abs/2411.13378
Autor:
Nguyen, Quang Vinh, Son, Vo Hoang Thanh, Hoang, Chau Truong Vinh, Nguyen, Duc Duy, Minh, Nhat Huy Nguyen, Kim, Soo-Hyung
Naturalistic driving action localization task aims to recognize and comprehend human behaviors and actions from video data captured during real-world driving scenarios. Previous studies have shown great action localization performance by applying a r
Externí odkaz:
http://arxiv.org/abs/2411.12525
Autor:
Nguyen, Viet Anh, Nguyen, Linh Thi Dieu, Do, Thi Thu Ha, Wu, Ye, Sergeev, Aleksandr A., Zhu, Ding, Valuckas, Vytautas, Pham, Duong, Bui, Hai Xuan Son, Hoang, Duy Mai, Bui, Son Tung, Bui, Xuan Khuyen, Nguyen, Binh Thanh, Nguyen, Hai Son, Vu, Lam Dinh, Rogach, Andrey, Ha, Son Tung, Le-Van, Quynh
Publikováno v:
J Phys Chem Lett J Phys Chem Lett . 2024 Nov 14;15(45):11291-11299
Enhancing light emission from perovskite nanocrystal (NC) films is essential in light-emitting devices, as their conventional stacks often restrict the escape of emitted light. This work addresses this challenge by employing a TiO$_2$ grating to enha
Externí odkaz:
http://arxiv.org/abs/2411.12463
In this paper, we propose ZeFaV - a zero-shot based fact-checking verification framework to enhance the performance on fact verification task of large language models by leveraging the in-context learning ability of large language models to extract t
Externí odkaz:
http://arxiv.org/abs/2411.11247
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
Autor:
Nguyen, Thanh Tam, Ren, Zhao, Pham, Trinh, Huynh, Thanh Trung, Nguyen, Phi Le, Yin, Hongzhi, Nguyen, Quoc Viet Hung
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require significant expertise, limiting accessibility. Recent strides in instruc
Externí odkaz:
http://arxiv.org/abs/2411.09955
Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) in knowledge-intensive tasks such as those from medical domain. However, the sensitive nature of the medical domain ne
Externí odkaz:
http://arxiv.org/abs/2411.09213
Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like the SIR family
Externí odkaz:
http://arxiv.org/abs/2411.06781