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Found 7 papers, 7 papers with code
Audience-Centric Natural Language Generation via Style Infusion
While existing approaches demonstrate textual style transfer with large volumes of parallel or non-parallel data, we argue that grounding style on audience-independent external factors is innately limiting for two reasons.
Sparsity-aware neural user behavior modeling in online interaction platforms
With the rapid proliferation of such online services, learning data-driven user behavior models is indispensable to enable personalized user experiences.
Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework
We present InfoMotif, a new semi-supervised, motif-regularized, learning framework over graphs.
GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation
We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together.
Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction
Recent years have witnessed tremendous interest in understanding and predicting information spread on social media platforms such as Twitter, Facebook, etc.
Dynamic Graph Representation Learning via Self-Attention Networks
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization.
Motif-based Convolutional Neural Network on Graphs
This paper introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas.
