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Found 5 papers, 3 papers with code
FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction
State-of-the-art DocRE methods use a graph structure to connect entities across the document to capture dependency syntax information.
Learning Multi-graph Structure for Temporal Knowledge Graph Reasoning
Concretely, it comprises three distinct modules concentrating on multiple aspects of graph structure knowledge within TKGs, including concurrent and evolutional patterns along timestamps, query-specific correlations across timestamps, and semantic dependencies of timestamps, which capture TKG features from various perspectives.
Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs.
Ranked #1 on
Fine-Grained Urban Flow Inference
on TaxiBJ-P4
Behavior Recognition Based on the Integration of Multigranular Motion Features
Specifically, behavior recognition may even rely more on the modeling of temporal information containing short-range and long-range motions; this contrasts with computer vision tasks involving images that focus on the understanding of spatial information.
Memory-Associated Differential Learning
Conventional Supervised Learning approaches focus on the mapping from input features to output labels.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
