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pro vyhledávání: '"Beng, A"'
With the release of open source datasets such as nuPlan and Argoverse, the research around learning-based planners has spread a lot in the last years. Existing systems have shown excellent capabilities in imitating the human driver behaviour, but the
Externí odkaz:
http://arxiv.org/abs/2409.02871
Classical learning of the expectation values of observables for quantum states is a natural variant of learning quantum states or channels. While learning-theoretic frameworks establish the sample complexity and the number of measurement shots per sa
Externí odkaz:
http://arxiv.org/abs/2408.05116
Autor:
Zhao, Zhanhao, Cai, Shaofeng, Gao, Haotian, Pan, Hexiang, Xiang, Siqi, Xing, Naili, Chen, Gang, Ooi, Beng Chin, Shen, Yanyan, Wu, Yuncheng, Zhang, Meihui
Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry sectors. Nonetheless, most existing approaches fail to account for the
Externí odkaz:
http://arxiv.org/abs/2408.03013
Fraud detection presents a challenging task characterized by ever-evolving fraud patterns and scarce labeled data. Existing methods predominantly rely on graph-based or sequence-based approaches. While graph-based approaches connect users through sha
Externí odkaz:
http://arxiv.org/abs/2408.00513
Autor:
Dong, Xingbo, Zhang, Hui, Lai, Yen Lung, Jin, Zhe, Huang, Junduan, Kang, Wenxiong, Teoh, Andrew Beng Jin
Deriving a unique cryptographic key from biometric measurements is a challenging task due to the existing noise gap between the biometric measurements and error correction coding. Additionally, privacy and security concerns arise as biometric measure
Externí odkaz:
http://arxiv.org/abs/2407.14804
Autor:
Wei, Jianxin, Zhu, Yizheng, Xiao, Xiaokui, Bao, Ergute, Yang, Yin, Cai, Kuntai, Ooi, Beng Chin
Graph Convolutional Networks (GCNs) are a popular machine learning model with a wide range of applications in graph analytics, including healthcare, transportation, and finance. Similar to other neural networks, a GCN may memorize parts of the traini
Externí odkaz:
http://arxiv.org/abs/2407.05034
Autor:
Jung, Yoon Gyo, Park, Jaewoo, Dong, Xingbo, Park, Hojin, Teoh, Andrew Beng Jin, Camps, Octavia
Understanding the vulnerability of face recognition systems to malicious attacks is of critical importance. Previous works have focused on reconstructing face images that can penetrate a targeted verification system. Even in the white-box scenario, h
Externí odkaz:
http://arxiv.org/abs/2407.02403
Recently, finger knuckle prints (FKPs) have gained attention due to their rich textural patterns, positioning them as a promising biometric for identity recognition. Prior FKP recognition methods predominantly leverage first-order feature descriptors
Externí odkaz:
http://arxiv.org/abs/2406.19672
Cohort studies are of significant importance in the field of healthcare analysis. However, existing methods typically involve manual, labor-intensive, and expert-driven pattern definitions or rely on simplistic clustering techniques that lack medical
Externí odkaz:
http://arxiv.org/abs/2406.14015
Autor:
Zhao, Yunpeng, Chen, Cheng, Pang, Qing You, Li, Quanzheng, Tang, Carol, Ang, Beng-Ti, Jin, Yueming
Addressing missing modalities presents a critical challenge in multimodal learning. Current approaches focus on developing models that can handle modality-incomplete inputs during inference, assuming that the full set of modalities are available for
Externí odkaz:
http://arxiv.org/abs/2406.01987