Zobrazeno 1 - 10
of 8 378
pro vyhledávání: '"WEI, Chao"'
Recent advancements in large vision-language models (LVLM) have significantly enhanced their ability to comprehend visual inputs alongside natural language. However, a major challenge in their real-world application is hallucination, where LVLMs gene
Externí odkaz:
http://arxiv.org/abs/2412.02946
Reinforcement learning (RL) policies are prone to high-frequency oscillations, especially undesirable when deploying to hardware in the real-world. In this paper, we identify, categorize, and compare methods from the literature that aim to mitigate h
Externí odkaz:
http://arxiv.org/abs/2410.16632
Autor:
Wei, Chao-Chun, Li, Xiaoyin, Hatt, Sabrina, Huai, Xudong, Liu, Jue, Singh, Birender, Kim, Kyung-Mo, Fernandes, Rafael M., Cardon, Paul, Zhao, Liuyan, Tran, Thao T., Frandsen, Benjamin M., Burch, Kenneth S., Liu, Feng, Ji, Huiwen
Altermagnets represent a new class of magnetic phases without net magnetization that are invariant under a combination of rotation and time reversal. Unlike conventional collinear antiferromagnets (AFM), altermagnets could lead to new correlated stat
Externí odkaz:
http://arxiv.org/abs/2410.14542
Partial tomography, which focuses on reconstructing reduced density matrices (RDMs), has emerged as a promising approach for characterizing complex quantum systems, particularly when full state tomography is impractical. Recently, overlapping tomogra
Externí odkaz:
http://arxiv.org/abs/2410.13473
Autor:
Hu, Chang-Kang, Wei, Chao, Liu, Chilong, Che, Liangyu, Zhou, Yuxuan, Xie, Guixu, Qin, Haiyang, Hu, Guantian, Yuan, Haolan, Zhou, Ruiyang, Liu, Song, Tan, Dian, Xin, Tao, Yu, Dapeng
Publikováno v:
Phys. Rev. Lett. 133, 160801 (2024)
Quantum state tomography (QST) via local measurements on reduced density matrices (LQST) is a promising approach but becomes impractical for large systems. To tackle this challenge, we developed an efficient quantum state tomography method inspired b
Externí odkaz:
http://arxiv.org/abs/2409.12614
Detecting out-of-distribution (OOD) samples is crucial for trustworthy AI in real-world applications. Leveraging recent advances in representation learning and latent embeddings, Various scoring algorithms estimate distributions beyond the training d
Externí odkaz:
http://arxiv.org/abs/2409.12479
Autor:
Xie, Xiaojun, Wei, Chao, He, Xingchen, Chen, Yake, Wang, Chenghao, Sun, Jihui, Jiang, Lin, Ye, Jia, Zou, Xihua, Pan, Wei, Yan, Lianshan
The rapid advancement of the thin-film lithium niobate platform has established it as a premier choice for high-performance photonics integration. High-speed optical coherent receivers are essential for supporting the large communication capacities r
Externí odkaz:
http://arxiv.org/abs/2408.02878
Learning from noisy-labeled data is crucial for real-world applications. Traditional Noisy-Label Learning (NLL) methods categorize training data into clean and noisy sets based on the loss distribution of training samples. However, they often neglect
Externí odkaz:
http://arxiv.org/abs/2407.07331
We propose the expert composer policy, a framework to reliably expand the skill repertoire of quadruped agents. The composer policy links pair of experts via transitions to a sampled target state, allowing experts to be composed sequentially. Each ex
Externí odkaz:
http://arxiv.org/abs/2403.11412
Autor:
González, Sergio, Yi, Abel Ko-Chun, Hsieh, Wan-Ting, Chen, Wei-Chao, Wang, Chun-Li, Wu, Victor Chien-Chia, Chang, Shang-Hung
Publikováno v:
S. Gonz\'alez, A. K.-C. Yi, W.-T. Hsieh, W.-C. Chen, C.-L. Wang, V. C.-C. Wu, S.-H. Chang, Multi-modal heart failure risk estimation based on short ECG and sampled long-term HRV, Information Fusion 107 (2024) 102337
Cardiovascular diseases, including Heart Failure (HF), remain a leading global cause of mortality, often evading early detection. In this context, accessible and effective risk assessment is indispensable. Traditional approaches rely on resource-inte
Externí odkaz:
http://arxiv.org/abs/2403.15408