Zobrazeno 1 - 10
of 20 753
pro vyhledávání: '"Lisha, A."'
Autor:
Cui, Xiaodong, Saif, A F M, Lu, Songtao, Chen, Lisha, Chen, Tianyi, Kingsbury, Brian, Saon, George
In this paper, we propose a bilevel joint unsupervised and supervised training (BL-JUST) framework for automatic speech recognition. Compared to the conventional pre-training and fine-tuning strategy which is a disconnected two-stage process, BL-JUST
Externí odkaz:
http://arxiv.org/abs/2412.08548
Finding specific preference-guided Pareto solutions that represent different trade-offs among multiple objectives is critical yet challenging in multi-objective problems. Existing methods are restrictive in preference definitions and/or their theoret
Externí odkaz:
http://arxiv.org/abs/2412.01773
In this study, we propose GITSR, an effective framework for Graph Interaction Transformer-based Scene Representation for multi-vehicle collaborative decision-making in intelligent transportation system. In the context of mixed traffic where Connected
Externí odkaz:
http://arxiv.org/abs/2411.01608
Deep learning-based techniques have been widely utilized for brain tumor segmentation using both single and multi-modal Magnetic Resonance Imaging (MRI) images. Most current studies focus on centralized training due to the intrinsic challenge of data
Externí odkaz:
http://arxiv.org/abs/2409.01020
The Feistel Boomerang Connectivity Table ($\rm{FBCT}$), which is the Feistel version of the Boomerang Connectivity Table ($\rm{BCT}$), plays a vital role in analyzing block ciphers' ability to withstand strong attacks, such as boomerang attacks. Howe
Externí odkaz:
http://arxiv.org/abs/2408.11291
Due to the lack of fine-grained annotation guidance, current Multiple Instance Learning (MIL) struggles to establish a robust causal relationship between Whole Slide Image (WSI) diagnosis and evidence sub-images, just like fully supervised learning.
Externí odkaz:
http://arxiv.org/abs/2407.17157
Autor:
Chen, Linqing, Wang, Weilei, Bai, Zilong, Xu, Peng, Fang, Yan, Fang, Jie, Wu, Wentao, Zhou, Lizhi, Zhang, Ruiji, Xia, Yubin, Xu, Chaobo, Hu, Ran, Xu, Licong, Cai, Qijun, Hua, Haoran, Sun, Jing, Liu, Jin, Qiu, Tian, Liu, Haowen, Hu, Meng, Li, Xiuwen, Gao, Fei, Wang, Yufu, Tie, Lin, Wang, Chaochao, Lu, Jianping, Sun, Cheng, Wang, Yixin, Yang, Shengjie, Li, Yuancheng, Jin, Lu, Zhang, Lisha, Bian, Fu, Ye, Zhongkai, Pei, Lidong, Tu, Changyang
Large language models (LLMs) have revolutionized Natural Language Processing (NLP) by minimizing the need for complex feature engineering. However, the application of LLMs in specialized domains like biopharmaceuticals and chemistry remains largely u
Externí odkaz:
http://arxiv.org/abs/2406.18045
In the Vision-and-Language Navigation (VLN) task, the agent is required to navigate to a destination following a natural language instruction. While learning-based approaches have been a major solution to the task, they suffer from high training cost
Externí odkaz:
http://arxiv.org/abs/2405.10620
Autor:
Bai, Zilong, Zhang, Ruiji, Chen, Linqing, Cai, Qijun, Zhong, Yuan, Wang, Cong, Fang, Yan, Fang, Jie, Sun, Jing, Wang, Weikuan, Zhou, Lizhi, Hua, Haoran, Qiu, Tian, Wang, Chaochao, Sun, Cheng, Lu, Jianping, Wang, Yixin, Xia, Yubin, Hu, Meng, Liu, Haowen, Xu, Peng, Xu, Licong, Bian, Fu, Gu, Xiaolong, Zhang, Lisha, Wang, Weilei, Tu, Changyang
In recent years, large language models(LLMs) have attracted significant attention due to their exceptional performance across a multitude of natural language process tasks, and have been widely applied in various fields. However, the application of l
Externí odkaz:
http://arxiv.org/abs/2404.18255
Magnetic-array-type current sensors have garnered increasing popularity owing to their notable advantages, including broadband functionality, a large dynamic range, cost-effectiveness, and compact dimensions. However, the susceptibility of the measur
Externí odkaz:
http://arxiv.org/abs/2402.11419