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An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics
Long documents such as academic articles and business reports have been the standard format to detail out important issues and …
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Beyond low-pass filtering: Graph convolutional networks with automatic filtering
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph …
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Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and …
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Projective Ranking-based GNN Evasion Attacks
Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial …
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Reinforced, Incremental and Cross-lingual Event Detection From Social Messages
Detecting hot social events (e.g. political scandal, momentous meetings, natural hazards, etc.) from social messages iscrucial as it …
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Robust Physical-World Attacks on Face Recognition
Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many …
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks …
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TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal …
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Graph self-supervised learning: A survey
Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised …
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Predicting Best-Selling New Products in a Major Promotion Campaign through Graph Convolutional Networks
Many e-commerce platforms, such as AliExpress, run major promotion campaigns regularly. Before such a promotion, it is important to …