Zobrazeno 1 - 10
of 309
pro vyhledávání: '"Le Vu, P"'
Monitored Natural Attenuation (MNA) is gaining prominence as an effective method for managing soil and groundwater contamination due to its cost-efficiency and minimal environmental disruption. Despite its benefits, MNA necessitates extensive groundw
Externí odkaz:
http://arxiv.org/abs/2411.10214
Quantum computing has emerged as a powerful tool for solving complex computational problems, but access to real quantum hardware remains limited due to high costs and increasing demand for efficient quantum simulations. Unfortunately, software simula
Externí odkaz:
http://arxiv.org/abs/2411.04471
Autor:
Le, Vu-Anh, Dik, Mehmet
This paper presents a mathematics-informed approach to neural operator design, building upon the theoretical framework established in our prior work. By integrating rigorous mathematical analysis with practical design strategies, we aim to enhance th
Externí odkaz:
http://arxiv.org/abs/2411.01763
Autor:
Le, Vu-Anh, Dik, Mehmet
Neural operators have emerged as transformative tools for learning mappings between infinite-dimensional function spaces, offering useful applications in solving complex partial differential equations (PDEs). This paper presents a rigorous mathematic
Externí odkaz:
http://arxiv.org/abs/2410.21481
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
Autor:
Murugadoss, Bhuvanashree, Poelitz, Christian, Drosos, Ian, Le, Vu, McKenna, Nick, Negreanu, Carina Suzana, Parnin, Chris, Sarkar, Advait
LLMs-as-a-judge is a recently popularized method which replaces human judgements in task evaluation (Zheng et al. 2024) with automatic evaluation using LLMs. Due to widespread use of RLHF (Reinforcement Learning from Human Feedback), state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2408.08781
Autor:
Tran, Van Duy, Le, Tran Xuan Hieu, Tran, Thi Diem, Pham, Hoai Luan, Le, Vu Trung Duong, Vu, Tuan Hai, Nguyen, Van Tinh, Nakashima, Yasuhiko
Kolmogorov-Arnold Networks (KANs), a novel type of neural network, have recently gained popularity and attention due to the ability to substitute multi-layer perceptions (MLPs) in artificial intelligence (AI) with higher accuracy and interoperability
Externí odkaz:
http://arxiv.org/abs/2407.17790
Autor:
Singh, Usneek, Cambronero, José, Gulwani, Sumit, Kanade, Aditya, Khatry, Anirudh, Le, Vu, Singh, Mukul, Verbruggen, Gust
Large language models (LLMs) can be leveraged to help with writing formulas in spreadsheets, but resources on these formulas are scarce, impacting both the base performance of pre-trained models and limiting the ability to fine-tune them. Given a cor
Externí odkaz:
http://arxiv.org/abs/2407.10657
In this paper, we study the connectedness of the commuting graph of a general Lie algebra and provide a process to determine whether the commuting graph is connected or not, as well as to compute an upper bound for its diameter. In addition, we will
Externí odkaz:
http://arxiv.org/abs/2405.06521
Autor:
Singha, Ananya, Chopra, Bhavya, Khatry, Anirudh, Gulwani, Sumit, Henley, Austin Z., Le, Vu, Parnin, Chris, Singh, Mukul, Verbruggen, Gust
Automated insight generation is a common tactic for helping knowledge workers, such as data scientists, to quickly understand the potential value of new and unfamiliar data. Unfortunately, automated insights produced by large-language models can gene
Externí odkaz:
http://arxiv.org/abs/2405.01556