Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Bao, Yongjun"'
Autor:
Xiong, Yizhe, Chen, Hui, Hao, Tianxiang, Lin, Zijia, Han, Jungong, Zhang, Yuesong, Wang, Guoxin, Bao, Yongjun, Ding, Guiguang
Recently, the scale of transformers has grown rapidly, which introduces considerable challenges in terms of training overhead and inference efficiency in the scope of task adaptation. Existing works, namely Parameter-Efficient Fine-Tuning (PEFT) and
Externí odkaz:
http://arxiv.org/abs/2403.09192
Autor:
Wang, Siqi, Yang, Hailong, Wang, Xuezhu, Liu, Tongxuan, Wang, Pengbo, Liang, Xuning, Ma, Kejie, Feng, Tianyu, You, Xin, Bao, Yongjun, Liu, Yi, Luan, Zhongzhi, Qian, Depei
Large language models (LLM) have recently attracted surging interest due to their outstanding capabilities across various domains. However, enabling efficient LLM inference is challenging due to its autoregressive decoding that generates tokens only
Externí odkaz:
http://arxiv.org/abs/2402.15678
Autor:
Ding, Zixuan, Wang, Ao, Chen, Hui, Zhang, Qiang, Liu, Pengzhang, Bao, Yongjun, Yan, Weipeng, Han, Jungong
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023
Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, \ie, CLIP, to compensate for insufficient annotations. In spite of promising perf
Externí odkaz:
http://arxiv.org/abs/2303.13223
Conducting experiments with objectives that take significant delays to materialize (e.g. conversions, add-to-cart events, etc.) is challenging. Although the classical "split sample testing" is still valid for the delayed feedback, the experiment will
Externí odkaz:
http://arxiv.org/abs/2202.00846
Autor:
Carrion, Carlos, Wang, Zenan, Nair, Harikesh, Luo, Xianghong, Lei, Yulin, Lin, Xiliang, Chen, Wenlong, Hu, Qiyu, Peng, Changping, Bao, Yongjun, Yan, Weipeng
In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior. The former content helps advertisers achieve their marketing goals and provides a stream of ad
Externí odkaz:
http://arxiv.org/abs/2105.13556
Publikováno v:
In Digital Communications and Networks February 2024 10(1):1-6
Autor:
Zhan, Haolan, Zhang, Hainan, Chen, Hongshen, Shen, Lei, Ding, Zhuoye, Bao, Yongjun, Yan, Weipeng, Lan, Yanyan
In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to
Externí odkaz:
http://arxiv.org/abs/2103.01594
Click through rate(CTR) prediction is a core task in advertising systems. The booming e-commerce business in our company, results in a growing number of scenes. Most of them are so-called long-tail scenes, which means that the traffic of a single sce
Externí odkaz:
http://arxiv.org/abs/2011.11938
Autor:
Liu, Hu, Lu, Jing, Zhao, Xiwei, Xu, Sulong, Peng, Hao, Liu, Yutong, Zhang, Zehua, Li, Jian, Jin, Junsheng, Bao, Yongjun, Yan, Weipeng
Click-through rate (CTR) prediction is one of the fundamental tasks for e-commerce search engines. As search becomes more personalized, it is necessary to capture the user interest from rich behavior data. Existing user behavior modeling algorithms d
Externí odkaz:
http://arxiv.org/abs/2010.00985
Autor:
Cai, Hengyi, Chen, Hongshen, Song, Yonghao, Ding, Zhuoye, Bao, Yongjun, Yan, Weipeng, Zhao, Xiaofang
Neural dialogue response generation has gained much popularity in recent years. Maximum Likelihood Estimation (MLE) objective is widely adopted in existing dialogue model learning. However, models trained with MLE objective function are plagued by th
Externí odkaz:
http://arxiv.org/abs/2009.07543