Zobrazeno 1 - 10
of 52 308
pro vyhledávání: '"Huang, Wei A."'
Autor:
Lian, Haoran, Chen, Junmin, Huang, Wei, Xiong, Yizhe, Hu, Wenping, Ding, Guiguang, Chen, Hui, Niu, Jianwei, Lin, Zijia, Zhang, Fuzheng, Zhang, Di
Recently, Large language models (LLMs) have revolutionized Natural Language Processing (NLP). Pretrained LLMs, due to limited training context size, struggle with handling long token sequences, limiting their performance on various downstream tasks.
Externí odkaz:
http://arxiv.org/abs/2412.07171
Autor:
Huang, Wei-Lun, Xue, Minghao, Liu, Zhiyou, Tashayyod, Davood, Kang, Jun, Gandjbakhche, Amir, Kazhdan, Misha, Armand, Mehran
Melanoma is the most deadly form of skin cancer. Tracking the evolution of nevi and detecting new lesions across the body is essential for the early detection of melanoma. Despite prior work on longitudinal tracking of skin lesions in 3D total body p
Externí odkaz:
http://arxiv.org/abs/2412.07132
In this paper, we introduce a novel framework consisting of hybrid bit-level and generative semantic communications for efficient downlink image transmission within space-air-ground integrated networks (SAGINs). The proposed model comprises multiple
Externí odkaz:
http://arxiv.org/abs/2412.05647
Autor:
Depoutovitch, Alex, Chen, Chong, Chen, Jin, Larson, Paul, Lin, Shu, Ng, Jack, Cui, Wenlin, Liu, Qiang, Huang, Wei, Xiao, Yong, He, Yongjun
Publikováno v:
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
Using cloud Database as a Service (DBaaS) offerings instead of on-premise deployments is increasingly common. Key advantages include improved availability and scalability at a lower cost than on-premise alternatives. In this paper, we describe the de
Externí odkaz:
http://arxiv.org/abs/2412.02792
Autor:
Pan, Bikang, Li, Qun, Tang, Xiaoying, Huang, Wei, Fang, Zhen, Liu, Feng, Wang, Jingya, Yu, Jingyi, Shi, Ye
The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite its promise, real-world datasets often contain noisy labels that can d
Externí odkaz:
http://arxiv.org/abs/2412.01256
In this study, the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the cl
Externí odkaz:
http://arxiv.org/abs/2412.01173
The predominant success of diffusion models in generative modeling has spurred significant interest in understanding their theoretical foundations. In this work, we propose a feature learning framework aimed at analyzing and comparing the training dy
Externí odkaz:
http://arxiv.org/abs/2412.01021
Autor:
Han, Songhao, Huang, Wei, Shi, Hairong, Zhuo, Le, Su, Xiu, Zhang, Shifeng, Zhou, Xu, Qi, Xiaojuan, Liao, Yue, Liu, Si
The advancement of Large Vision Language Models (LVLMs) has significantly improved multimodal understanding, yet challenges remain in video reasoning tasks due to the scarcity of high-quality, large-scale datasets. Existing video question-answering (
Externí odkaz:
http://arxiv.org/abs/2411.14794
Wall-modeled large-eddy simulation (WMLES) is widely recognized as a useful method for simulation of turbulent flows at high Reynolds numbers. Nevertheless, a continual issue in different wall models is the shift of the mean velocity profile from the
Externí odkaz:
http://arxiv.org/abs/2411.12402
Machine learning interatomic potentials (MLIPs) have substantially advanced atomistic simulations in materials science and chemistry by providing a compelling balance between accuracy and computational efficiency. While leading MLIPs rely on represen
Externí odkaz:
http://arxiv.org/abs/2411.12096