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Found 7 papers, 1 papers with code
Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond
This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and beyond.
Targeted Background Removal Creates Interpretable Feature Visualizations
Feature visualization is used to visualize learned features for black box machine learning models.
Revisiting the Fragility of Influence Functions
In the last few years, many works have tried to explain the predictions of deep learning models.
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning model.
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review
Recent deep learning frameworks such as Graph Neural Networks (GNNs) and Transformers have shown remarkable success in multimodal learning.
Transformers in Time-series Analysis: A Tutorial
Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision.
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations.
