December 2020 – Andrew Fairless, Ph.D.

What I Read: Flows for simultaneous manifold learning and density estimation

By Andrew Fairless on December 31, 2020December 20, 2020

https://arxiv.org/abs/2003.13913 Flows for simultaneous manifold learning and density estimationJohann Brehmer, Kyle Cranmer “We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold asContinue readingWhat I Read: Flows for simultaneous manifold learning and density estimation

What I Read: Scientists Taught Mice to Smell an Odor That Doesn’t Exist

By Andrew Fairless on December 31, 2020December 20, 2020

https://www.wired.com/story/scientists-taught-mice-to-smell-an-odor-that-doesnt-exist/ Grace HuckinsScience06.25.2020 07:00 AMScientists Taught Mice to Smell an Odor That Doesn’t ExistWith direct brain stimulation, mice learned to recognize an imaginary scent—and helped researchers understand a key pieceContinue readingWhat I Read: Scientists Taught Mice to Smell an Odor That Doesn’t Exist

What I Read: To Adapt to Tech, We’re Heading Into the Shadows

By Andrew Fairless on December 30, 2020December 20, 2020

https://www.wired.com/story/opinion-to-adapt-to-tech-were-heading-into-the-shadows/ Matt BeaneIdeas06.08.2020 09:59 AMTo Adapt to Tech, We’re Heading Into the ShadowsMore and more innovation requires going into darker, more inappropriate, less ethical territory. Here’s how to respond. “…I’veContinue readingWhat I Read: To Adapt to Tech, We’re Heading Into the Shadows

What I Read: Learning Neural Causal Models

By Andrew Fairless on December 30, 2020December 20, 2020

https://arxiv.org/abs/1910.01075Learning Neural Causal Models from Unknown InterventionsNan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio “We present a new framework for meta-learning causal modelsContinue readingWhat I Read: Learning Neural Causal Models

What I Read: Fundamental Theorem for Epidemiology

By Andrew Fairless on December 29, 2020December 20, 2020

https://www.datasciencecentral.com/profiles/blogs/a-fundamental-theorem-for-epidemiology A Fundamental Theorem for EpidemiologyPosted by Peter Cotton on May 28, 2020 at 5:30am “This approach is represented in the picture above. The important thing to note is thatContinue readingWhat I Read: Fundamental Theorem for Epidemiology

What I Read: Penrose, Mathematical Notation to Beautiful Diagrams

By Andrew Fairless on December 29, 2020December 20, 2020

https://www.cs.cmu.edu/~kmcrane/Projects/Penrose/Penrose_SIGGRAPH.pdfPenrose: From Mathematical Notation to Beautiful Diagrams “We introduce a system called Penrose for creating mathematical diagrams. Its basic functionality is to translate abstract statements written in familiar math-like notationContinue readingWhat I Read: Penrose, Mathematical Notation to Beautiful Diagrams

What I Read: GPT-3, The New Mighty Language Model

By Andrew Fairless on December 28, 2020December 20, 2020

https://towardsdatascience.com/gpt-3-the-new-mighty-language-model-from-openai-a74ff35346fc?gi=a6255cca8370 GPT-3: The New Mighty Language Model from OpenAIPushing Deep Learning to the Limit with 175B ParametersMoiz SaifeeMay 31 “While this type of transfer learning obviates the need to useContinue readingWhat I Read: GPT-3, The New Mighty Language Model

What I Read: Self Supervised Representation Learning in NLP

By Andrew Fairless on December 28, 2020December 20, 2020

https://amitness.com/2020/05/self-supervised-learning-nlp/ Self Supervised Representation Learning in NLPPublished May 23, 2020 in nlp “While Computer Vision is making amazing progress on self-supervised learning only in the last few years, self-supervised learningContinue readingWhat I Read: Self Supervised Representation Learning in NLP

What I Read: ML models in production

By Andrew Fairless on December 27, 2020December 20, 2020

https://medium.com/analytics-and-data/overview-of-the-different-approaches-to-putting-machinelearning-ml-models-in-production-c699b34abf86 Overview of the different approaches to putting Machine Learning (ML) models in productionJulien KervizicApr 29, 2019 “There are different approaches to putting models into productions, with benefits that canContinue readingWhat I Read: ML models in production

What I Read: Symbolic Mathematics, Neural Networks

By Andrew Fairless on December 27, 2020December 20, 2020

https://www.quantamagazine.org/symbolic-mathematics-finally-yields-to-neural-networks-20200520/ artificial intelligenceSymbolic Mathematics Finally Yields to Neural NetworksAfter translating some of math’s complicated equations, researchers have created an AI system that they hope will answer even bigger questions.by StephenContinue readingWhat I Read: Symbolic Mathematics, Neural Networks

Month: December 2020