interpretability – Page 2 – Andrew Fairless, Ph.D.

What I Read: Features Are Important?

By Andrew Fairless on November 2, 2023October 5, 2023

https://www.kdnuggets.com/your-features-are-important-it-doesnt-mean-they-are-good Your Features Are Important? It Doesn’t Mean They Are GoodBy Samuele Mazzanti, Lead Data Scientist at JAKALASeptember 21, 2023 ““Feature Importance” is not enough. You also need to lookContinue readingWhat I Read: Features Are Important?

What I Read: Bonsai Networks, RNNs

By Andrew Fairless on October 3, 2023September 4, 2023

https://cprimozic.net/blog/growing-sparse-computational-graphs-with-rnns/ Growing Bonsai Networks with RNNsCasey Primozic “This writeup introduces what I’m calling Bonsai Networks – extremely sparse computational graphs produced by training and pruning RNNs. They provide an interpretableContinue readingWhat I Read: Bonsai Networks, RNNs

What I Read: Models Memorize or Generalize?

By Andrew Fairless on October 2, 2023September 4, 2023

https://pair.withgoogle.com/explorables/grokking/ Do Machine Learning Models Memorize or Generalize?By Adam Pearce, Asma Ghandeharioun, Nada Hussein, Nithum Thain, Martin Wattenberg and Lucas DixonAugust 2023 “Large language models can certainly seem like theyContinue readingWhat I Read: Models Memorize or Generalize?

What I Read: Shapley Values

By Andrew Fairless on March 2, 2023January 9, 2023

https://www.aidancooper.co.uk/how-shapley-values-work/ How Shapley Values WorkAidan CooperDec 31, 2022 “Shapley values… are machine learning explainability techniques…. we will explore how Shapley values work – not using cryptic formulae, but by wayContinue readingWhat I Read: Shapley Values

What I Read: Biases, Saliency

By Andrew Fairless on February 9, 2023December 16, 2022

https://pair.withgoogle.com/explorables/saliency/ Searching for Unintended Biases With SaliencyAstrid Bertrand, Adam Pearce and Nithum ThainDecember 2022 “If there are spurious correlations in the training data, the model might develop unintended biases thatContinue readingWhat I Read: Biases, Saliency

What I Read: explainability, survival analysis

By Andrew Fairless on November 21, 2022September 27, 2022

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…Continue readingWhat I Read: explainability, survival analysis

What I Read: Visual Explanation of Classifiers

By Andrew Fairless on March 23, 2022February 3, 2022

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…Continue readingWhat I Read: Visual Explanation of Classifiers

What I Read: Aristotle, Deep Learning

By Andrew Fairless on March 17, 2022March 12, 2022

https://thegradient.pub/how-aristotle-is-fixing-deep-learnings-flaws/ How Aristotle is Fixing Deep Learning’s FlawsPaul J. Blazek17.Feb.2022 “…Aristotle’s logic still stands strong, poignantly describing the building blocks of human reasoning. Yet many of the key ingredients describedContinue readingWhat I Read: Aristotle, Deep Learning

What I Read: Interpretable Time Series

By Andrew Fairless on February 14, 2022January 20, 2022

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 atContinue readingWhat I Read: Interpretable Time Series

What I Read: Non-Technical Guide to Interpreting SHAP

By Andrew Fairless on December 13, 2021November 16, 2021

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 forContinue readingWhat I Read: Non-Technical Guide to Interpreting SHAP

Tag: interpretability