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Found 6 papers, 0 papers with code
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs
Then, we further design a pre-training and domain adaptation framework to extract the transferable and generalizable features so that different COs can benefit from them.
Multi-task Self-distillation for Graph-based Semi-Supervised Learning
In this paper, we propose a multi-task self-distillation framework that injects self-supervised learning and self-distillation into graph convolutional networks to separately address the mismatch problem from the structure side and the label side.
Latent Network Embedding via Adversarial Auto-encoders
To address this issue, we propose a latent network embedding model based on adversarial graph auto-encoders.
Transformed $\ell_1$ Regularization for Learning Sparse Deep Neural Networks
To this end, we introduce a new non-convex integrated transformed $\ell_1$ regularizer to promote sparsity for DNNs, which removes both redundant connections and unnecessary neurons simultaneously.
A Novel Large-scale Ordinal Regression Model
Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels.
Diffusion Based Network Embedding
In order to solve the limitations, we propose in this paper a network diffusion based embedding method.
