Search Results for author: Lifan Zhao

Found 7 papers, 3 papers with code

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

1 code implementation31 Jan 2024 Lifan Zhao, Yanyan Shen

Recently, channel-independent methods have achieved state-of-the-art performance in multivariate time series (MTS) forecasting.

Multivariate Time Series Forecasting Time Series

Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey

no code implementations9 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.

Stock Price Prediction

DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

no code implementations16 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.

Incremental Learning Meta-Learning

RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction

1 code implementation28 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.

Click-Through Rate Prediction Meta-Learning +2

Mixed Information Flow for Cross-domain Sequential Recommendations

1 code implementation1 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.

Sequential Recommendation Transfer Learning

Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification

no code implementations21 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).

General Classification Image Classification

Integrating Parametric and Non-Parametric Models For Scene Labeling

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.

General Classification Metric Learning +1

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