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Found 8 papers, 5 papers with code
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
Recently, interpretable machine learning has re-explored concept bottleneck models (CBM), comprising step-by-step prediction of the high-level concepts from the raw features and the target variable from the predicted concepts.
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
We propose Signal is Harder (SiH), a variational-autoencoder-based method that simultaneously trains a biased and unbiased classifier using a novel, disentangling reweighting scheme inspired by the focal loss.
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries.
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets.
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
In addition, we compare several intra- and post-processing approaches applied to debiasing deep chest X-ray classifiers.
A Deep Variational Approach to Clustering Survival Data
In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task.
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
Exploratory analysis of time series data can yield a better understanding of complex dynamical systems.
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
In this review, we examine the problem of designing interpretable and explainable machine learning models.
Counterfactual Explanation
Explainable artificial intelligence
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