Zobrazeno 1 - 10
of 855
pro vyhledávání: '"Wang, YiQi"'
Autor:
Han, Xiaotian, Jian, Yiren, Hu, Xuefeng, Liu, Haogeng, Wang, Yiqi, Fan, Qihang, Ai, Yuang, Huang, Huaibo, He, Ran, Yang, Zhenheng, You, Quanzeng
Pre-training on large-scale, high-quality datasets is crucial for enhancing the reasoning capabilities of Large Language Models (LLMs), especially in specialized domains such as mathematics. Despite the recognized importance, the Multimodal LLMs (MLL
Externí odkaz:
http://arxiv.org/abs/2409.12568
Top-k Nearest Neighbors (kNN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large search space, a plethora of complex-index-based approaches have been propos
Externí odkaz:
http://arxiv.org/abs/2408.05432
Autor:
Yang, Xihong, Wang, Yiqi, Chen, Jin, Fan, Wenqi, Zhao, Xiangyu, Zhu, En, Liu, Xinwang, Lian, Defu
Deep learning has been widely applied in recommender systems, which has achieved revolutionary progress recently. However, most existing learning-based methods assume that the user and item distributions remain unchanged between the training phase an
Externí odkaz:
http://arxiv.org/abs/2407.15620
Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions. In this work, we develop a new approach of assessing model domain and demonstrate that our approach provides accura
Externí odkaz:
http://arxiv.org/abs/2406.05143
Autor:
Zhang, Jiaxin, Wang, Yiqi, Yang, Xihong, Wang, Siwei, Feng, Yu, Shi, Yu, Ren, Ruicaho, Zhu, En, Liu, Xinwang
Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectiveness of GNNs. To mitigate this challenge, Test-Time Training (TTT) has b
Externí odkaz:
http://arxiv.org/abs/2404.13571
Autor:
Liu, Haogeng, You, Quanzeng, Han, Xiaotian, Wang, Yiqi, Zhai, Bohan, Liu, Yongfei, Tao, Yunzhe, Huang, Huaibo, He, Ran, Yang, Hongxia
Multimodal Large Language Models (MLLMs) have experienced significant advancements recently. Nevertheless, challenges persist in the accurate recognition and comprehension of intricate details within high-resolution images. Despite being indispensabl
Externí odkaz:
http://arxiv.org/abs/2403.01487
Autor:
Hu, Jiaxi, Gao, Jingtong, Zhao, Xiangyu, Hu, Yuehong, Liang, Yuxuan, Wang, Yiqi, He, Ming, Liu, Zitao, Yin, Hongzhi
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research. In the initial stages of multimodal sequential recommendation models, the mainstream paradigm was ID-dominant recomm
Externí odkaz:
http://arxiv.org/abs/2402.17334
Autor:
Wang, Maolin, Pan, Yu, Xu, Zenglin, Guo, Ruocheng, Zhao, Xiangyu, Wang, Wanyu, Wang, Yiqi, Liu, Zitao, Liu, Langming
Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contributed to the advancement of recommendation systems and information retrie
Externí odkaz:
http://arxiv.org/abs/2402.00388
Autor:
Guo, Xianda, Zhang, Chenming, Lu, Juntao, Wang, Yiqi, Duan, Yiqun, Yang, Tian, Zhu, Zheng, Chen, Long
Stereo matching aims to estimate the disparity between matching pixels in a stereo image pair, which is important to robotics, autonomous driving, and other computer vision tasks. Despite the development of numerous impressive methods in recent years
Externí odkaz:
http://arxiv.org/abs/2312.00343
Autor:
Han, Xiaotian, You, Quanzeng, Liu, Yongfei, Chen, Wentao, Zheng, Huangjie, Mrini, Khalil, Lin, Xudong, Wang, Yiqi, Zhai, Bohan, Yuan, Jianbo, Wang, Heng, Yang, Hongxia
Multi-modal Large Language Models (MLLMs) are increasingly prominent in the field of artificial intelligence. These models not only excel in traditional vision-language tasks but also demonstrate impressive performance in contemporary multi-modal ben
Externí odkaz:
http://arxiv.org/abs/2311.11567