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Found 9 papers, 4 papers with code
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications
In this paper, we develop data-dependent and algorithm-dependent generalization bounds for transductive learning algorithms in the context of information theory for the first time.
Perfect Alignment May be Poisonous to Graph Contrastive Learning
Graph Contrastive Learning (GCL) aims to learn node representations by aligning positive pairs and separating negative ones.
DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs
To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts.
Can Large Language Models Empower Molecular Property Prediction?
Molecular property prediction has gained significant attention due to its transformative potential in multiple scientific disciplines.
Towards Understanding the Generalization of Graph Neural Networks
After that, we provide high probability bounds of generalization gap for popular GNNs.
Deep Safe Multi-View Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase
However, we observe that learning from data with more views is not guaranteed to achieve better clustering performance than from data with fewer views.
Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering
The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views.
Multi-level Feature Learning for Contrastive Multi-view Clustering
Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces.
Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering
To leverage the multi-view complementary information, we concatenate all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures.
