Context-aware citation recommendation aims to automatically predict suitable citations for a given citation context, which is essentially helpful for researchers when writing scientific papers. In existing neural network-based approaches, overcorrelation in the weight matrix influences semantic similarity, which is a difficult problem to solve. In this paper, we propose a novel context-aware citation recommendation approach that can essentially improve the orthogonality of the weight matrix and explore more accurate citation patterns. We quantitatively show that the various reference patterns in the paper have interactional features that can significantly affect link prediction. We conduct experiments on the CiteSeer datasets. The results show that our model is superior to baseline models in all metrics.

Differentiable architecture search (DARTS) has received great attention due to its simplicity and efficiency. However, there are two annoying problems. One is that searched architecture of normal cell tends to be shallow. The other is skip-connect aggregation caused by the unfair competition between operations. We find that fewer operations per edge is helpful to search for deeper architectures, so we divide the operations into groups and train these groups in turn. To explore competitiveness among all the operations in the search space, candidate operations will be regrouped in each epoch. In addition, the random grouping prevents the overfitting of the super network, and consequently avoids the skip-connect aggregation. We named this method GroupDARTS and evaluated these searched architectures, achieving a state-of-the-art result of 97.68% on CIFAR10 and a top-1 accuray of 75.5% on ImageNet.