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Found 8 papers, 0 papers with code
AISFG: Abundant Information Slot Filling Generator
As an essential component of task-oriented dialogue systems, slot filling requires enormous labeled training data in a certain domain.
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing
Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph.
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces
To address the aforementioned issues, we propose a novel Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces, CSINCERE.
SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds
To explore these issues for sequential interaction networks, we propose SINCERE, a novel method representing Sequential Interaction Networks on Co-Evolving RiEmannian manifolds.
Contrastive Graph Clustering in Curvature Spaces
On the other hand, contrastive learning boosts the deep graph clustering but usually struggles in either graph augmentation or hard sample mining.
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence.
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning
To bridge this gap, we make the first attempt to study the problem of self-supervised temporal graph representation learning in the general Riemannian space, supporting the time-varying curvature to shift among hyperspherical, Euclidean and hyperbolic spaces.
A Self-supervised Mixed-curvature Graph Neural Network
Instead of working on one single constant-curvature space, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces and design hierarchical attention mechanisms for learning and fusing the representations across these component spaces.
