Zobrazeno 1 - 10
of 935
pro vyhledávání: '"Vu, Trung"'
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
By using the squared slack variables technique, we show that a general polynomial complementarity problem can be formulated as a system of polynomial equations. Thus, the solution set of such a problem is the image of a real algebraic set under a cer
Externí odkaz:
http://arxiv.org/abs/2410.21810
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:
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
In this paper, we propose criteria for unboundedness of the images of set-valued mappings having closed graphs in Euclidean spaces. We focus on mappings whose domains are non-closed or whose values are connected. These criteria allow us to see struct
Externí odkaz:
http://arxiv.org/abs/2312.14783
Independent component analysis (ICA) is now a widely used solution for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent vector analysis (IVA) generalizes ICA to multiple datasets, i.e., to multi-subject dat
Externí odkaz:
http://arxiv.org/abs/2311.05049
Autor:
Hieu, Vu Trung, Takeda, Akiko
In this paper, we focus on computing local minimizers of a multivariate polynomial optimization problem under certain genericity conditions. By using a technique in computer algebra and the second-order optimality condition, we provide a univariate r
Externí odkaz:
http://arxiv.org/abs/2311.00838
Autor:
Duy, Phan The, Khoa, Nghi Hoang, Quyen, Nguyen Huu, Trinh, Le Cong, Kien, Vu Trung, Hoang, Trinh Minh, Pham, Van-Hau
This paper presents VulnSense framework, a comprehensive approach to efficiently detect vulnerabilities in Ethereum smart contracts using a multimodal learning approach on graph-based and natural language processing (NLP) models. Our proposed framewo
Externí odkaz:
http://arxiv.org/abs/2309.08474
Autor:
Singh, Anima, Vu, Trung, Mehta, Nikhil, Keshavan, Raghunandan, Sathiamoorthy, Maheswaran, Zheng, Yilin, Hong, Lichan, Heldt, Lukasz, Wei, Li, Tandon, Devansh, Chi, Ed H., Yi, Xinyang
Randomly-hashed item ids are used ubiquitously in recommendation models. However, the learned representations from random hashing prevents generalization across similar items, causing problems of learning unseen and long-tail items, especially when i
Externí odkaz:
http://arxiv.org/abs/2306.08121
Autor:
Rajput, Shashank, Mehta, Nikhil, Singh, Anima, Keshavan, Raghunandan H., Vu, Trung, Heldt, Lukasz, Hong, Lichan, Tay, Yi, Tran, Vinh Q., Samost, Jonah, Kula, Maciej, Chi, Ed H., Sathiamoorthy, Maheswaran
Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2305.05065