Zobrazeno 1 - 10
of 79 884
pro vyhledávání: '"Duong AT"'
We investigate the problem of agent-to-agent interaction in decentralized (federated) learning over time-varying directed graphs, and, in doing so, propose a consensus-based algorithm called DSGTm-TV. The proposed algorithm incorporates gradient trac
Externí odkaz:
http://arxiv.org/abs/2409.17189
Autor:
Long, Do Xuan, Yen, Duong Ngoc, Luu, Anh Tuan, Kawaguchi, Kenji, Kan, Min-Yen, Chen, Nancy F.
We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language model (LLM) generation. Specifically, it guides an LLM to fulfill an input instruction by simulating multiple experts,
Externí odkaz:
http://arxiv.org/abs/2411.00492
Autor:
Duong, Manh Hong, Han, The Anh
Uncertainty, characterised by randomness and stochasticity, is ubiquitous in applications of evolutionary game theory across various fields, including biology, economics and social sciences. The uncertainty may arise from various sources such as fluc
Externí odkaz:
http://arxiv.org/abs/2410.22010
Homomorphic permutations are fundamental to privacy-preserving computations based on word-wise homomorphic encryptions, which can be accelerated through permutation decomposition. This paper defines an ideal performance of any decomposition on permut
Externí odkaz:
http://arxiv.org/abs/2410.21840
We investigate a family-nonuniversal Abelian extension of hypercharge, which significantly alters the phenomenological features of the standard model. Anomaly cancellation requires that the third quark family transforms differently from the first two
Externí odkaz:
http://arxiv.org/abs/2410.15635
Feature Selection (FS) under domain adaptation (DA) is a critical task in machine learning, especially when dealing with limited target data. However, existing methods lack the capability to guarantee the reliability of FS under DA. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2410.15022
Image dehazing is crucial for clarifying images obscured by haze or fog, but current learning-based approaches is dependent on large volumes of training data and hence consumed significant computational power. Additionally, their performance is often
Externí odkaz:
http://arxiv.org/abs/2410.14595
Autor:
Chen, Meng, Arthur, Philip, Feng, Qianyu, Hoang, Cong Duy Vu, Hong, Yu-Heng, Moghaddam, Mahdi Kazemi, Nezami, Omid, Nguyen, Thien, Tangari, Gioacchino, Vu, Duy, Vu, Thanh, Johnson, Mark, Kenthapadi, Krishnaram, Dharmasiri, Don, Duong, Long, Li, Yuan-Fang
Large language models (LLMs) have shown impressive performance in \emph{code} understanding and generation, making coding tasks a key focus for researchers due to their practical applications and value as a testbed for LLM evaluation. Data synthesis
Externí odkaz:
http://arxiv.org/abs/2411.00005
Autor:
Baharvandi, Arash, Nguyen, Duong Tung
This paper examines the integrated generation and transmission expansion planning problem to address the growing challenges associated with increasing power network loads. The proposed approach optimizes the operation and investment costs for new gen
Externí odkaz:
http://arxiv.org/abs/2410.12508
Quantum emulators play an important role in the development and testing of quantum algorithms, especially given the limitations of the current FTQC era. Developing high-speed, memory-optimized quantum emulators is a growing research trend, with gate
Externí odkaz:
http://arxiv.org/abs/2410.11146