interpretability – Page 4 – Andrew Fairless, Ph.D.

What I Read: Deep learning, black box

By Andrew Fairless on March 21, 2021March 7, 2021

https://bdtechtalks.com/2021/01/11/concept-whitening-interpretable-neural-networks/ Deep learning doesn’t need to be a black boxBen DicksonJanuary 11, 2021 “The innerworkings of neural networks are often a mystery…. scientists at Duke University propose “concept whitening,” aContinue readingWhat I Read: Deep learning, black box

What I Read: Interpretation for Image Recognition

By Andrew Fairless on March 12, 2021March 7, 2021

https://thegradient.pub/a-visual-history-of-interpretation-for-image-recognition/ A Visual History of Interpretation for Image Recognition16.Jan.2021by Ali Abdalla “…during the last decade, researchers developed many different methods to open the “black box” of deep learning, aiming toContinue readingWhat I Read: Interpretation for Image Recognition

What I Read: definitive guide to AI monitoring

By Andrew Fairless on March 7, 2021March 7, 2021

https://towardsdatascience.com/the-definitive-guide-to-ai-monitoring-2427812cc1b?gi=d1344496bc82 The definitive guide to AI monitoringLearning from our work creating production visibility for teams across deep learning and machine learning use casesYotam OrenNov 3, 2020 “AI teams across verticalsContinue readingWhat I Read: definitive guide to AI monitoring

What I Read: Deploying Machine Learning, a Survey of Case Studies

By Andrew Fairless on February 23, 2021February 3, 2021

https://arxiv.org/abs/2011.09926 Challenges in Deploying Machine Learning: a Survey of Case StudiesAndrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence “This survey reviews published reports of deploying machine learning solutions in a varietyContinue readingWhat I Read: Deploying Machine Learning, a Survey of Case Studies

What I Read: Interpretability in Machine Learning

By Andrew Fairless on February 20, 2021January 21, 2021

https://thegradient.pub/interpretability-in-ml-a-broad-overview/ Interpretability in Machine Learning: An Overview21.Nov.2020Owen Shen “Broadly, interpretability focuses on the how. It’s focused on getting some notion of an explanation for the decisions made by our models….Continue readingWhat I Read: Interpretability in Machine Learning

What I Read: Explainable AI, 2-Stage Approach

By Andrew Fairless on February 5, 2021January 10, 2021

https://www.kdnuggets.com/2020/10/explaining-explainable-ai.html Explaining the Explainable AI: A 2-Stage ApproachBy Amit Dhurandhar, Research Scientist at IBM. “XAI is primarily concerned with understanding or interpreting the decisions made by these opaque or black-boxContinue readingWhat I Read: Explainable AI, 2-Stage Approach

What I Read: “Less than one”-shot learning

By Andrew Fairless on February 4, 2021January 10, 2021

https://www.technologyreview.com/2020/10/16/1010566/ai-machine-learning-with-tiny-data/ Artificial intelligence/Machine learningA radical new technique lets AI learn with practically no data“Less than one”-shot learning can teach a model to identify more objects than the number of examplesContinue readingWhat I Read: “Less than one”-shot learning

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