Search Results for author: Xinjiang Lu

Found 6 papers, 5 papers with code

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

1 code implementation8 Jan 2023 Yan Li, Xinjiang Lu, Yaqing Wang, Dejing Dou

In this work, we propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D3VAE.

Denoising Disentanglement +2

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach

1 code implementation5 Jan 2023 Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Jingbo Zhou, Dejing Dou, Hui Xiong

Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables.

Descriptive Language Modelling +1

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

1 code implementation5 Jan 2023 Yan Li, Xinjiang Lu, Haoyi Xiong, Jian Tang, Jiantao Su, Bo Jin, Dejing Dou

Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning.

Time Series Time Series Forecasting +1

SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022

1 code implementation8 Aug 2022 Jingbo Zhou, Xinjiang Lu, Yixiong Xiao, Jiantao Su, Junfu Lyu, Yanjun Ma, Dejing Dou

Thus, Wind Power Forecasting (WPF) has been widely recognized as one of the most critical issues in wind power integration and operation.

Out-of-Town Recommendation with Travel Intention Modeling

1 code implementation29 Jan 2021 Haoran Xin, Xinjiang Lu, Tong Xu, Hao liu, Jingjing Gu, Dejing Dou, Hui Xiong

Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM).

point of interests

Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction

no code implementations18 Jan 2021 Qiang Zhou, Jingjing Gu, Xinjiang Lu, Fuzhen Zhuang, Yanchao Zhao, Qiuhong Wang, Xiao Zhang

Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites.

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