Zobrazeno 1 - 10
of 11 782
pro vyhledávání: '"Nguyen Xuan, A."'
Jointly optimizing power allocation and device association is crucial in Internet-of-Things (IoT) networks to ensure devices achieve their data throughput requirements. Device association, which assigns IoT devices to specific access points (APs), cr
Externí odkaz:
http://arxiv.org/abs/2411.10082
This paper proposes a distributed learning-based framework to tackle the sum ergodic rate maximization problem in cell-free massive multiple-input multiple-output (MIMO) systems by utilizing the graph neural network (GNN). Different from centralized
Externí odkaz:
http://arxiv.org/abs/2411.02900
This paper presents a novel approach to designing a Hedge Algebra Controller named Hedge Algebra Controller with Recursive Semantic Values (RS-HAC). This approach incorporates several newly introduced concepts, including Semantically Quantifying Simp
Externí odkaz:
http://arxiv.org/abs/2410.15058
Autor:
Seo, Minseok, Nguyen, Xuan Truong, Hwang, Seok Joong, Kwon, Yongkee, Kim, Guhyun, Park, Chanwook, Kim, Ilkon, Park, Jaehan, Kim, Jeongbin, Shin, Woojae, Won, Jongsoon, Choi, Haerang, Kim, Kyuyoung, Kwon, Daehan, Jeong, Chunseok, Lee, Sangheon, Choi, Yongseok, Byun, Wooseok, Baek, Seungcheol, Lee, Hyuk-Jae, Kim, John
Publikováno v:
ASPLOS 2024
Accelerating end-to-end inference of transformer-based large language models (LLMs) is a critical component of AI services in datacenters. However, diverse compute characteristics of end-to-end LLM inference present challenges as previously proposed
Externí odkaz:
http://arxiv.org/abs/2410.15008
Grasping a variety of objects remains a key challenge in the development of versatile robotic systems. The human hand is remarkably dexterous, capable of grasping and manipulating objects with diverse shapes, mechanical properties, and textures. Insp
Externí odkaz:
http://arxiv.org/abs/2410.05789
Autor:
Pham, The Hieu, Nguyen, Phuong Thanh Tran, Nguyen, Xuan Tho, Nguyen, Tan Dat, Nguyen, Duc Dung
The research on audio clue-based target speaker extraction (TSE) has mostly focused on modeling the mixture and reference speech, achieving high performance in English due to the availability of large datasets. However, less attention has been given
Externí odkaz:
http://arxiv.org/abs/2410.00527
Autor:
Ming, Yifei, Purushwalkam, Senthil, Pandit, Shrey, Ke, Zixuan, Nguyen, Xuan-Phi, Xiong, Caiming, Joty, Shafiq
Ensuring faithfulness to context in large language models (LLMs) and retrieval-augmented generation (RAG) systems is crucial for reliable deployment in real-world applications, as incorrect or unsupported information can erode user trust. Despite adv
Externí odkaz:
http://arxiv.org/abs/2410.03727
Autor:
Tran, Quyen, Thanh, Nguyen Xuan, Anh, Nguyen Hoang, Hai, Nam Le, Le, Trung, Van Ngo, Linh, Nguyen, Thien Huu
Few-shot Continual Relations Extraction (FCRE) is an emerging and dynamic area of study where models can sequentially integrate knowledge from new relations with limited labeled data while circumventing catastrophic forgetting and preserving prior kn
Externí odkaz:
http://arxiv.org/abs/2410.00334
Large Language Models (LLMs) have demonstrated remarkable capabilities in handling long context inputs, but this comes at the cost of increased computational resources and latency. Our research introduces a novel approach for the long context bottlen
Externí odkaz:
http://arxiv.org/abs/2409.17422
Autor:
Nguyen, Xuan-Phi, Pandit, Shrey, Purushwalkam, Senthil, Xu, Austin, Chen, Hailin, Ming, Yifei, Ke, Zixuan, Savarese, Silvio, Xong, Caiming, Joty, Shafiq
Retrieval Augmented Generation (RAG), a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance, has emerged as a pivotal area in generative AI. The LLMs used in RAG applica
Externí odkaz:
http://arxiv.org/abs/2409.09916