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Found 6 papers, 2 papers with code
Certification for Differentially Private Prediction in Gradient-Based Training
Differential privacy upper-bounds the information leakage of machine learning models, yet providing meaningful privacy guarantees has proven to be challenging in practice.
Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples
We study the problem of explaining link predictions in the Knowledge Graph Embedding (KGE) models.
Machine Learning-Assisted Recurrence Prediction for Early-Stage Non-Small-Cell Lung Cancer Patients
Conclusions: Our results show that machine learning models trained on tabular and graph data can enable objective, personalised and reproducible prediction of relapse and therefore, disease outcome in patients with early-stage NSCLC.
Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data
We have applied our method to a deep learning model for semantic segmentation, U-Net, in a remote sensing application of building detection - one of the core use cases of remote sensing in humanitarian applications.
Discovering Concepts in Learned Representations using Statistical Inference and Interactive Visualization
Among current formulations, concepts defines them by as a direction in a learned representation space.
Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset
In previous studies, the base method is applied to the classification of cardiac disease and provides clinically meaningful explanations for the predictions of a black-box deep learning classifier.
