machine learning – Page 46 – Andrew Fairless, Ph.D.

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: Architectures for Modern Data Infrastructure

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

https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/ Emerging Architectures for Modern Data Infrastructureby Matt Bornstein, Martin Casado, and Jennifer Li “We’re now starting to see the rise of massive, complex systems built around data – whereContinue readingWhat I Read: Architectures for Modern Data Infrastructure

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: Reinforcement learning is supervised learning

By Andrew Fairless on February 2, 2021January 8, 2021

https://bair.berkeley.edu/blog/2020/10/13/supervised-rl/ Reinforcement learning is supervised learning on optimized dataBen Eysenbach and Aviral Kumar and Abhishek Gupta Oct 13, 2020 “This suggests that we can simply use inverse RL to relabelContinue readingWhat I Read: Reinforcement learning is supervised learning

What I Read: Software Tips for Data Science

By Andrew Fairless on February 2, 2021January 8, 2021

https://medium.com/swlh/software-engineering-tips-and-best-practices-for-data-science-5d85dbcf87fd Software Engineering Tips and Best Practices for Data ScienceWith great code comes great machine learningAhmed BesbesAug 28 “However, jupyter notebooks hide some serious pitfalls that may turn your codingContinue readingWhat I Read: Software Tips for Data Science

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

What I Read: Can a neural network train other networks?

By Andrew Fairless on January 31, 2021January 6, 2021

https://towardsdatascience.com/can-a-neural-network-train-other-networks-cf371be516c6?gi=4765ee11be44 Can a neural network train other networks?An introduction to knowledge distillationTivadar DankaOct 5 “Now you have a huge model, which, although performs excellently, there is no way to deployContinue readingWhat I Read: Can a neural network train other networks?

What I Read: Transformers for Image Recognition

By Andrew Fairless on January 31, 2021January 6, 2021

https://medium.com/swlh/an-image-is-worth-16×16-words-transformers-for-image-recognition-at-scale-brief-review-of-the-8770a636c6a8 An Image Is Worth 16×16 Words: Transformers for Image Recognition at Scale (Brief Review of the ICLR 2021 Paper)Stan KriventsovOct 9 “The reason attention models haven’t been doing betterContinue readingWhat I Read: Transformers for Image Recognition

What I Read: automatic differentiation with graphs

By Andrew Fairless on January 30, 2021January 5, 2021

https://ai.facebook.com/blog/a-new-open-source-framework-for-automatic-differentiation-with-graphs A new open source framework for automatic differentiation with graphsOctober 8th, 2020 “Just as PyTorch provides a framework for automatic differentiation with tensors, GTN provides such a framework forContinue readingWhat I Read: automatic differentiation with graphs

Tag: machine learning