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

What I Read: Data-efficient image Transformers

By Andrew Fairless on March 5, 2021March 5, 2021

Data-efficient image Transformers: A promising new technique for image classificationDecember 23, 2020 “We’ve developed a new method to train computer vision models that leverage Transformers…. Our new technique — Data-efficientContinue readingWhat I Read: Data-efficient image Transformers

What I Read: NeRF Explosion 2020

By Andrew Fairless on February 28, 2021February 23, 2021

https://dellaert.github.io/NeRF/NeRF Explosion 2020Published: December 16, 2020 “Neural volume rendering refers to methods that generate images or video by tracing a ray into the scene and taking an integral of someContinue readingWhat I Read: NeRF Explosion 2020

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: HuggingFace Transformers

By Andrew Fairless on February 18, 2021January 18, 2021

https://medium.com/georgian-impact-blog/how-to-incorporate-tabular-data-with-huggingface-transformers-b70ac45fcfb4 How to Incorporate Tabular Data with HuggingFace TransformersGeorgianOct 23 “At Georgian, we find ourselves working with supporting tabular feature information as well as unstructured text data. We found thatContinue readingWhat I Read: HuggingFace Transformers

What I Read: Revisiting Sutton’s Bitter Lesson for AI

By Andrew Fairless on February 15, 2021January 18, 2021

https://blog.exxactcorp.com/compute-goes-brrr-revisiting-suttons-bitter-lesson-artificial-intelligence/ Deep LearningCompute Goes Brrr: Revisiting Sutton’s Bitter Lesson for Artificial IntelligenceMarketing, October 27, 2020 “The main driver of AI progress, according to Sutton, is the increasing availability of computeContinue readingWhat I Read: Revisiting Sutton’s Bitter Lesson for AI

What I Read: AI for good, think form extraction

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

http://jonathanstray.com/to-apply-ai-for-good-think-form-extraction To apply AI for good, think form extractionPosted on October 24, 2020 Author Jonathan Stray “Folks who want to use AI/ML for good generally think of things like buildingContinue readingWhat I Read: AI for good, think form extraction

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: What Color Is This? Part 2

By Andrew Fairless on February 3, 2021January 8, 2021

https://multithreaded.stitchfix.com/blog/2020/10/13/what-color-is-this-part-2/ What Color Is This? Part 2Blog post by Dan TasseDan TasseOctober 13, 2020 – San Francisco, CAWhat Color Is This, Part 2: The Computational Parts “We have to findContinue readingWhat I Read: What Color Is This? Part 2

What I Read: Neural Networks Help Explain Brains

By Andrew Fairless on February 1, 2021January 7, 2021

https://www.quantamagazine.org/deep-neural-networks-help-to-explain-living-brains-20201028/ neuroscienceDeep Neural Networks Help to Explain Living Brains Deep neural networks, often criticized as “black boxes,” are helping neuroscientists understand the organization of living brains.“If their CNN mimicked aContinue readingWhat I Read: Neural Networks Help Explain Brains

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