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Found 6 papers, 5 papers with code
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification
Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed augmentation rules to generate contrastive samples, which can potentially corrupt or distort the original information.
Distilling Conditional Diffusion Models for Offline Reinforcement Learning through Trajectory Stitching
Deep generative models have recently emerged as an effective approach to offline reinforcement learning.
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model.
Structural Entropy Guided Graph Hierarchical Pooling
In addition to SEP, we further design two classification models, SEP-G and SEP-N for graph classification and node classification, respectively.
A Simple yet Effective Method for Graph Classification
Inspired by structural entropy on graphs, we transform the data sample from graphs to coding trees, which is a simpler but essential structure for graph data.
Price graphs: Utilizing the structural information of financial time series for stock prediction
Then, structural information, referring to associations among temporal points and the node weights, is extracted from the mapped graphs to resolve the problems regarding long-range dependencies and the chaotic property.
