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Found 4 papers, 2 papers with code
Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network
In real-world applications, events are not always observable, and estimating event time is as important as predicting future events.
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation
The fundamental problem with these studies is that they ignore the evolution of services over time and the representation gap between services and requirements.
Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism
However, relying only on aggregation to propagate information in dynamic graphs can result in delays in information propagation and thus affect the performance of the method.
LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition
In this paper, we first examine traditional active learning strategies in a specific case of BiLstm-CRF that has widely used in named entity recognition on several typical datasets.
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