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

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

What I Read: AI Can Help Patients If Doctors Understand It

By Andrew Fairless on January 27, 2021January 1, 2021

https://www.wired.com/story/ai-help-patients-doctors-understand/ Tom SimoniteBusiness10.02.2020 08:00 AMAI Can Help Patients—but Only If Doctors Understand It Algorithms can help diagnose a growing range of health problems, but humans need to be trained toContinue readingWhat I Read: AI Can Help Patients If Doctors Understand It

What I Read: Maintaining Machine Learning in Production

By Andrew Fairless on January 19, 2021December 27, 2020

https://eugeneyan.com/writing/practical-guide-to-maintaining-machine-learning/ A Practical Guide to Maintaining Machine Learning in Production “This follow-up will share some practices I’ve found useful to maintaining machine learning in production. They are: Monitor Training andContinue readingWhat I Read: Maintaining Machine Learning in Production

What I Read: making machine learning actually useful

By Andrew Fairless on January 5, 2021December 22, 2020

https://www.shreya-shankar.com/making-ml-work/ Shreya ShankarReflecting on a year of making machine learning actually usefulJune 29, 2020 in #machine learning · 11 min read “…ML research as a field puts too much emphasisContinue readingWhat I Read: making machine learning actually useful

What I Read: Nitpicking ML Technical Debt

By Andrew Fairless on January 2, 2021December 22, 2020

https://matthewmcateer.me/blog/machine-learning-technical-debt/ Nitpicking Machine Learning Technical DebtRevisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)May 10, 2020 “Now the Tech Debt Paper is makingContinue readingWhat I Read: Nitpicking ML Technical Debt

What I Read: Introduction to Circuits

By Andrew Fairless on December 18, 2020December 4, 2020

https://distill.pub/2020/circuits/zoom-in/ Zoom In: An Introduction to CircuitsBy studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks. “In contrast to the typical picture ofContinue readingWhat I Read: Introduction to Circuits

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