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Found 9 papers, 9 papers with code
Graph Entropy Minimization for Semi-supervised Node Classification
Node classifiers are required to comprehensively reduce prediction errors, training resources, and inference latency in the industry.
Unifying Label-inputted Graph Neural Networks with Deep Equilibrium Models
The unification facilitates the exchange between the two subdomains and inspires more studies.
Scalable Multi-view Clustering with Graph Filtering
With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years.
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
Following the application of Deep Learning to graphic data, Graph Neural Networks (GNNs) have become the dominant method for Node Classification on graphs in recent years.
Ranked #1 on
Node Classification
on Amazon Photo
Self-supervised Consensus Representation Learning for Attributed Graph
Self-supervised loss is designed to maximize the agreement of the embeddings of the same node in the topology graph and the feature graph.
Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering
The success of subspace clustering depends on the assumption that the data can be separated into different subspaces.
Adversarial Privacy Preserving Graph Embedding against Inference Attack
In this paper, we propose Adversarial Privacy Graph Embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations.
Fine-Grained Image Captioning with Global-Local Discriminative Objective
This is primarily due to (i) the conservative characteristic of traditional training objectives that drives the model to generate correct but hardly discriminative captions for similar images and (ii) the uneven word distribution of the ground-truth captions, which encourages generating highly frequent words/phrases while suppressing the less frequent but more concrete ones.
Sparse Label Smoothing Regularization for Person Re-Identification
Finally, we assign a non-uniform label distribution to the generated samples and define a regularized loss function for training.
