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Found 7 papers, 1 papers with code
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs.
A Causal Framework to Unify Common Domain Generalization Approaches
Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s).
Model Debiasing via Gradient-based Explanation on Representation
Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem.
Contrastive Domain Generalization via Logit Attribution Matching
Domain Generalization (DG) is an important open problem in machine learning.
Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction
There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis.
CELNet: Evidence Localization for Pathology Images using Weakly Supervised Learning
Despite deep convolutional neural networks boost the performance of image classification and segmentation in digital pathology analysis, they are usually weak in interpretability for clinical applications or require heavy annotations to achieve object localization.
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network
Although convolutional neural networks (CNN) have advantages in extracting discriminative features in image classification, directly training a CNN on high resolution histology images is computationally infeasible currently.
