Zobrazeno 1 - 10
of 774
pro vyhledávání: '"Liu, Xiaoyuan"'
Autor:
Liu, Xiaoyuan, Wang, Wenxuan, Yuan, Youliang, Huang, Jen-tse, Liu, Qiuzhi, He, Pinjia, Tu, Zhaopeng
This paper explores the problem of commonsense-level vision-knowledge conflict in Multimodal Large Language Models (MLLMs), where visual information contradicts model's internal commonsense knowledge (see Figure 1). To study this issue, we introduce
Externí odkaz:
http://arxiv.org/abs/2410.08145
In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuits. This ensures that a range of proposed applications can be deployed
Externí odkaz:
http://arxiv.org/abs/2408.13352
Deep neural networks (DNNs) have achieved significant success in numerous applications. The remarkable performance of DNNs is largely attributed to the availability of massive, high-quality training datasets. However, processing such massive training
Externí odkaz:
http://arxiv.org/abs/2407.10446
Autor:
Wang, Yuxuan, Liu, Xiaoyuan
Scene Graph Generation (SGG) provides basic language representation of visual scenes, requiring models to grasp complex and diverse semantics between various objects. However, this complexity and diversity in SGG also leads to underrepresentation, wh
Externí odkaz:
http://arxiv.org/abs/2403.16184
Autor:
Gill, Sukhpal Singh, Cetinkaya, Oktay, Marrone, Stefano, Claudino, Daniel, Haunschild, David, Schlote, Leon, Wu, Huaming, Ottaviani, Carlo, Liu, Xiaoyuan, Machupalli, Sree Pragna, Kaur, Kamalpreet, Arora, Priyansh, Liu, Ji, Farouk, Ahmed, Song, Houbing Herbert, Uhlig, Steve, Ramamohanarao, Kotagiri
The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing. These quantum features help solve many complex prob
Externí odkaz:
http://arxiv.org/abs/2403.02240
Autor:
Li, Qinbin, Xie, Chulin, Xu, Xiaojun, Liu, Xiaoyuan, Zhang, Ce, Li, Bo, He, Bingsheng, Song, Dawn
Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data. However, most existing federated learning studies focus on either horizontal or
Externí odkaz:
http://arxiv.org/abs/2310.11865
Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have made remarkable progress in many physical scenarios, especially in communication applications. Des
Externí odkaz:
http://arxiv.org/abs/2310.09800
Autor:
Herman, Dylan, Googin, Cody, Liu, Xiaoyuan, Sun, Yue, Galda, Alexey, Safro, Ilya, Pistoia, Marco, Alexeev, Yuri
Publikováno v:
Nature Reviews Physics (2023)
Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehensive summary of the state of the art of quantum computing for financi
Externí odkaz:
http://arxiv.org/abs/2307.11230
The shortest path network interdiction (SPNI) problem poses significant computational challenges due to its NP-hardness. Current solutions, primarily based on integer programming methods, are inefficient for large-scale instances. In this paper, we i
Externí odkaz:
http://arxiv.org/abs/2307.07577
Autor:
Galda, Alexey, Gupta, Eesh, Falla, Jose, Liu, Xiaoyuan, Lykov, Danylo, Alexeev, Yuri, Safro, Ilya
The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. A near-optimal solution to the combinatorial optimization problem is ac
Externí odkaz:
http://arxiv.org/abs/2307.05420