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Found 10 papers, 5 papers with code
Graph Augmentation for Recommendation
To address these challenges, we propose a principled framework called GraphAug.
Improving Factual Error Correction by Learning to Inject Factual Errors
Given the lack of paired data (i. e., false claims and their corresponding correct claims), existing methods typically adopt the mask-then-correct paradigm.
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
In addition, we introduce a cross-view contrastive learning paradigm to model the inter-dependencies across view-specific region representations and preserve underlying relation heterogeneity.
Automated Spatio-Temporal Graph Contrastive Learning
In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources.
On Inferring User Socioeconomic Status with Mobility Records
The DeepSEI model incorporates two networks called deep network and recurrent network, which extract the features of the mobility records from three aspects, namely spatiality, temporality and activity, one at a coarse level and the other at a detailed level.
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Detecting anomalous trajectories has become an important task in many location-based applications.
Deep Learning Based Defect Detection for Solder Joints on Industrial X-Ray Circuit Board Images
With some advanced algorithms, the new technologies are expected to control the production quality based on the digital images.
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation
Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector.
PSDNet and DPDNet: Efficient channel expansion, Depthwise-Pointwise-Depthwise Inverted Bottleneck Block
To design more efficient lightweight concolutional neural netwok, Depthwise-Pointwise-Depthwise inverted bottleneck block (DPD block) is proposed and DPDNet is designed by stacking DPD block.
Recent Advances in Convolutional Neural Network Acceleration
We propose a taxonomy in terms of three levels, i. e.~structure level, algorithm level, and implementation level, for acceleration methods.
