Because of its wide application, critical nodes identification has become an important research topic at the micro level of network science.
In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.
In this paper, a highly efficient pruning method is proposed to significantly reduce the cost of pruning DCNN.
Many real-world systems can be expressed in temporal networks with nodes playing far different roles in structure and function and edges representing the relationships between nodes.
By representing products as nodes and their relationships as edges of a graph, we show how an inductive graph neural network approach, named GraphSAGE, can efficiently learn continuous representations for nodes and edges.
When multiple teacher models are available in distillation, the state-of-the-art methods assign a fixed weight to a teacher model in the whole distillation.
At top of the MoE layer, we deploy a transformer layer for each task as task tower to learn task-specific information.
We demonstrate the feasibility of this approach to the automatic identification, linking and resolution -- a task known as Wikification -- of learning resources mentioned on MOOC discussion forums, from a harvested collection of 100K+ resources.
Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages.