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OpenWGL: Open-World Graph Learning
In traditional graph learning tasks, such as node classification, the learning is carried out in a closed-world setting where the …
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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including …
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Grounding Visual Concepts for Multimedia Event Detection and Multimedia Event Captioning in Zero-shot Setting
The flourishing of social media platforms requires techniques for understanding the content of media on a large scale. However, …
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Multivariate Relations Aggregation Learning in Social Networks
Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, …
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A Relation-Specific Attention Network for Joint Entity and Relation Extraction
Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all …
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One-Shot Neural Architecture Search via Novelty Driven Sampling
One-Shot Neural architecture search (NAS) has received wide attention due to its computational efficiency. Many One-Shot NAS methods …
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Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning
Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge …
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Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization
One-Shot Neural Architecture Search (NAS) significantly improves the computational efficiency through weight sharing. However, this …
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Unsupervised Domain Adaptive Graph Convolutional Networks
Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only …
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Going Deep: Graph Convolutional Ladder-shape Networks
Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric …
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