activity
20172023
most citedContinuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction

41 citations · 249 across the 52 of their papers we have counts for

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Showing 2022Show all

22 papers · 1 filter

cs.LG2022

Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents

Meng Xiao, Dongjie Wang, Min Wu +5

Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data…

cs.LG2022★ 1 cited

Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations

Dongkun Wang, Wei Fan, Pengyang Wang +4

Urban traffic speed prediction aims to estimate the future traffic speed for improving the urban transportation services. Enormous efforts have been made on exploiting spatial corr…

eess.AS2022★ 1 cited

MIMO-DBnet: Multi-channel Input and Multiple Outputs DOA-aware Beamforming Network for Speech Separation

Yanjie Fu, Haoran Yin, Meng Ge +5

Recently, many deep learning based beamformers have been proposed for multi-channel speech separation. Nevertheless, most of them rely on extra cues known in advance, such as speak…

cs.LG2022★ 2 cited

GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation

Han Huang, Leilei Sun, Bowen Du +2

Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due t…

cs.AI2022★ 1 cited

Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning

Dongjie Wang, Lingfei Wu, Denghui Zhang +3

The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive.…

cs.IR2022★ 1 cited

Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective

Weijieying Ren, Lei Wang, Kunpeng Liu +3

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-t…