Zobrazeno 1 - 10
of 313
pro vyhledávání: '"Xu, Yonghui"'
Publikováno v:
Comptes Rendus. Mécanique, Vol 348, Iss 6-7, Pp 519-535 (2020)
A toroidal bubble or a cylindrical gas jet are known to be subjected to the Rayleigh–Plateau instability. Air bubble rings produced by beluga whales and dolphins however are observed that remain stable for long times. In the present work, we analys
Externí odkaz:
https://doaj.org/article/5717bc4c6b384dc0be3e943438ffa788
Food recommendation systems serve as pivotal components in the realm of digital lifestyle services, designed to assist users in discovering recipes and food items that resonate with their unique dietary predilections. Typically, multi-modal descripti
Externí odkaz:
http://arxiv.org/abs/2406.18962
Test-time adaptation (TTA) aims to adapt a model, initially trained on training data, to potential distribution shifts in the test data. Most existing TTA studies, however, focus on classification tasks, leaving a notable gap in the exploration of TT
Externí odkaz:
http://arxiv.org/abs/2310.05341
Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enabling unsupervised representation learning is extremely crucial for time s
Externí odkaz:
http://arxiv.org/abs/2308.01578
Federated domain adaptation (FDA) aims to collaboratively transfer knowledge from source clients (domains) to the related but different target client, without communicating the local data of any client. Moreover, the source clients have different dat
Externí odkaz:
http://arxiv.org/abs/2305.10432
Autor:
Zhang, Lei, Wang, Mingliang, Zhou, Xin, Wu, Xingyu, Cao, Yiming, Xu, Yonghui, Cui, Lizhen, Shen, Zhiqi
Delivery Time Estimation (DTE) is a crucial component of the e-commerce supply chain that predicts delivery time based on merchant information, sending address, receiving address, and payment time. Accurate DTE can boost platform revenue and reduce c
Externí odkaz:
http://arxiv.org/abs/2302.07429
Autor:
Wang, Shipeng, Li, Qingzhong, Cui, Lizhen, Yan, Zhongmin, Xu, Yonghui, Shi, Zhuan, Min, Xinping, Shen, Zhiqi, Yu, Han
Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the ye
Externí odkaz:
http://arxiv.org/abs/2212.14676
Autor:
Sun, Zehua, Xu, Yonghui, Liu, Yong, He, Wei, Kong, Lanju, Wu, Fangzhao, Jiang, Yali, Cui, Lizhen
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 2023
Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of the real use
Externí odkaz:
http://arxiv.org/abs/2301.00767
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absenc
Externí odkaz:
http://arxiv.org/abs/2212.01141
Autor:
Zhang, Yixin, Liu, Yong, Xu, Yonghui, Xiong, Hao, Lei, Chenyi, He, Wei, Cui, Lizhen, Miao, Chunyan
The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequen
Externí odkaz:
http://arxiv.org/abs/2205.14837