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Found 8 papers, 5 papers with code
Multi-modal Causal Structure Learning and Root Cause Analysis
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems.
FairGen: Towards Fair Graph Generation
In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability.
Fairness-aware Multi-view Clustering
We also derive insights regarding the relative performance of the proposed regularizers in various scenarios.
MentorGNN: Deriving Curriculum for Pre-Training GNNs
To comprehend heterogeneous graph signals at different granularities, we propose a curriculum learning paradigm that automatically re-weighs graph signals in order to ensure a good generalization in the target domain.
Deeper-GXX: Deepening Arbitrary GNNs
However, two major problems hinder the deeper GNNs to obtain satisfactory performance, i. e., vanishing gradient and over-smoothing.
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Heterogeneous Contrastive Learning
We first provide a theoretical analysis showing that the vanilla contrastive learning loss easily leads to the sub-optimal solution in the presence of false negative pairs, whereas the proposed weighted loss could automatically adjust the weight based on the similarity of the learned representations to mitigate this issue.
Deep Co-Attention Network for Multi-View Subspace Learning
For example, given the diagnostic result that a model provided based on the X-ray images of a patient at different poses, the doctor needs to know why the model made such a prediction.
Deep Multimodality Model for Multi-task Multi-view Learning
However, there is no existing deep learning algorithm that jointly models task and view dual heterogeneity, particularly for a data set with multiple modalities (text and image mixed data set or text and video mixed data set, etc.).
