37 citations · 52 across the 5 of their papers we have counts for
5 papers
Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples
Adrianna Janik, Luca Costabello
We study the problem of explaining link predictions in the Knowledge Graph Embedding (KGE) models. We propose an example-based approach that exploits the latent space representatio…
Machine Learning-Assisted Recurrence Prediction for Early-Stage Non-Small-Cell Lung Cancer Patients
Adrianna Janik, Maria Torrente, Luca Costabello +14
Background: Stratifying cancer patients according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to utili…
Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data
Adrianna Janik, Kris Sankaran
The use of remote sensing in humanitarian crisis response missions is well-established and has proven relevant repeatedly. One of the problems is obtaining gold annotations as it i…
Discovering Concepts in Learned Representations using Statistical Inference and Interactive Visualization
Adrianna Janik, Kris Sankaran
Concept discovery is one of the open problems in the interpretability literature that is important for bridging the gap between non-deep learning experts and model end-users. Among…
Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset
Adrianna Janik, Jonathan Dodd, Georgiana Ifrim +2
Cardiac Magnetic Resonance (CMR) is the most effective tool for the assessment and diagnosis of a heart condition, which malfunction is the world's leading cause of death. Software…