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.
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.
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.
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.
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.
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).
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.