Search Results for author:
Found 4 papers, 2 papers with code
Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction
To filter out irrelevant noises and alleviate the error propagation, DSAN dynamically extracts valuable information by applying self-attention over the noisy input and bridges each output directly to the purified inputs via implementing a switch-attention mechanism.
Interpretable Crowd Flow Prediction with Spatial-Temporal Self-Attention
Crowd flow prediction has been increasingly investigated in intelligent urban computing field as a fundamental component of urban management system.
Spatial-Temporal Self-Attention Network for Flow Prediction
Flow prediction (e. g., crowd flow, traffic flow) with features of spatial-temporal is increasingly investigated in AI research field.
Improving Abstractive Document Summarization with Salient Information Modeling
Comprehensive document encoding and salient information selection are two major difficulties for generating summaries with adequate salient information.
