https://medium.com/responsibleml/survex-model-agnostic-explainability-for-survival-analysis-94444e6ce83d survex: model-agnostic explainability for survival analysisMikołaj SpytekSep 19 “… survival models… tells us what is the probability of an event not happening until a given time t…. The complexity…
What I Read: Visual Explanation of Classifiers
https://ai.googleblog.com/2022/01/introducing-stylex-new-approach-for.html Introducing StylEx: A New Approach for Visual Explanation of ClassifiersTuesday, January 18, 2022Posted by Oran Lang and Inbar Mosseri, Software Engineers, Google Research“Previous approaches for visual explanations of classifiers…
What I Read: Interpretable Time Series
https://ai.googleblog.com/2021/12/interpretable-deep-learning-for-time.html Interpretable Deep Learning for Time Series ForecastingMonday, December 13, 2021Posted by Sercan O. Arik, Research Scientist and Tomas Pfister, Engineering Manager, Google Cloud “Multi-horizon forecasting, i.e. predicting variables-of-interest at
What I Read: Non-Technical Guide to Interpreting SHAP
https://www.aidancooper.co.uk/a-non-technical-guide-to-interpreting-shap-analyses/ Explaining Machine Learning Models: A Non-Technical Guide to Interpreting SHAP AnalysesAidan CooperNov 1, 2021With interpretability becoming an increasingly important requirement for machine learning projects, there’s a growing need for
What I Read: CNN Heat Maps, Class Activation Mapping
https://glassboxmedicine.com/2019/06/11/cnn-heat-maps-class-activation-mapping-cam/ CNN Heat Maps: Class Activation Mapping (CAM)Date: June 11, 2019Author: Rachel Draelos “Class Activation Mapping (CAM) is one technique for producing heat maps to highlight class-specific regions of images.”