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CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic …
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Multi-Graph Fusion Networks for Urban Region Embedding
Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the …
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Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the …
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Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting
A variety of real-world applications rely on far future information to make decisions, thus calling for efficient and accurate long …
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A Probabilistic Graphical Model Based on Neural-symbolic Reasoning for Visual Relationship Detection
This paper aims to leverage symbolic knowledge to improve the performance and interpretability of the Visual Relationship Detection …
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BaLeNAS: Differentiable Architecture Search via Bayesian Learning Rule
Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the …
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Cross-modal Clinical Graph Transformer For Ophthalmic Report Generation
Automatic generation of ophthalmic reports using data-driven neural networks has great potential in clinical practice. When writing a …
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Towards Spatio-Temporal Aware Traffic Time Series Forecasting
Traffic time series forecasting is challenging due to complex spatio-temporal dynamics—time series from different locations often have …
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Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realisation
Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a …
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Fire Burns, Swords Cut: Commonsense Inductive Bias for Exploration in Text-based Games
Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed …
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