no code implementations • CVPR 2015 • Bing Shuai, Gang Wang, Zhen Zuo, Bing Wang, Lifan Zhao
We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification.
no code implementations • 21 Aug 2015 • Zhen Zuo, Gang Wang, Bing Shuai, Lifan Zhao, Qingxiong Yang
In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL).
1 code implementation • 1 Dec 2020 • Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Lifan Zhao, Jun Ma, Maarten de Rijke
One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains.
1 code implementation • 28 Oct 2022 • Yanyan Shen, Lifan Zhao, Weiyu Cheng, Zibin Zhang, Wenwen Zhou, Kangyi Lin
Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users.
no code implementations • 16 Jun 2023 • Lifan Zhao, Shuming Kong, Yanyan Shen
To address this challenge, we propose DoubleAdapt, an end-to-end framework with two adapters, which can effectively adapt the data and the model to mitigate the effects of distribution shifts.
no code implementations • 9 Aug 2023 • Liping Wang, Jiawei Li, Lifan Zhao, Zhizhuo Kou, Xiaohan Wang, Xinyi Zhu, Hao Wang, Yanyan Shen, Lei Chen
Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market.
1 code implementation • 31 Jan 2024 • Lifan Zhao, Yanyan Shen
Recently, channel-independent methods have achieved state-of-the-art performance in multivariate time series (MTS) forecasting.