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- Yue GaoSuggested from co-authorship
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Works19 from public data
- Dynamic Hypergraph Neural Networks457
A dynamic hypergraph neural networks framework (DHGNN), which is composed of the stacked layers of two modules: dynamic hyper graph construction ( DHG) and hypergrpah convolution (HGC), which outperforms state-of-the-art methods.
- Divide and Conquer: Question-Guided Spatio-Temporal Contextual Attention for Video Question Answering133
Experimental results and comparisons with the state-of-the-art methods have shown that the proposed Question-Guided Spatio-Temporal Contextual Attention Network (QueST) method can achieve superior performance.
- MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval113
Experiments and comparisons show that the proposed MLVCNN method can achieve significant performance improvement on 3D shape retrieval tasks, and outperforms the state-of-the-art methods by the mAP of 4.84%.
- Self-supervised Motion Learning from Static Images33
The model learns to encode motion information by classifying pseudo motions generated by MoSI, and introduces a static mask in pseudo motions to create local motion patterns, which forces the model to additionally locate notable motion areas for the correct classification.
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- Short-term electricity consumption forecasting method based on empirical mode decomposition of long-short term memory network7
The proposed EMD-LSTM independent forecasting model is able to identify the characteristics of each frequency component of electricity consumption data, and its error is reduced by about 15% on average, thus achieving the goal of improving the accuracy of load forecasting in short-term electricity consumption forecasting scenarios.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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