Andrew Fairless – Page 76 – Andrew Fairless, Ph.D.

What I Read: GPT-3, a Giant Step for NLP

By Andrew Fairless on January 3, 2021December 22, 2020

https://anotherdatum.com/gpt-3.html GPT-3, a Giant Step for Deep Learning And NLPPosted on 03 June 2020 “…OpenAI announced a new successor to their Language Model (LM) – GPT-3. This is the largestContinue readingWhat I Read: GPT-3, a Giant Step for NLP

What I Read: Computers Roll Loaded Dice

By Andrew Fairless on January 3, 2021December 22, 2020

https://www.quantamagazine.org/how-and-why-computers-roll-loaded-dice-20200708/ Abstractions blogHow and Why Computers Roll Loaded DiceResearchers are one step closer to injecting probability into deterministic machines.Stephen OrnesContributing WriterJuly 8, 2020 “FLDR won’t give you an edge inContinue readingWhat I Read: Computers Roll Loaded Dice

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: Five Cognitive Biases

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

https://builtin.com/data-science/cognitive-biases-data-science Five Cognitive Biases in Data Science (and How to Avoid Them)Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work.RahulContinue readingWhat I Read: Five Cognitive Biases

What I Read: data distributions

By Andrew Fairless on January 1, 2021December 22, 2020

https://www.kdnuggets.com/2020/06/overview-data-distributions.html Overview of data distributionsTags: Binomial, Distribution, Normal Distribution, Poisson Distribution, Probability, StatisticsBy Madalina Ciortan, Data scientist, PhD researcher in bioinformatics at ULB “With so many types of data distributionsContinue readingWhat I Read: data distributions

What I Read: Differentiable Reasoning over Text

By Andrew Fairless on January 1, 2021December 22, 2020

https://blog.ml.cmu.edu/2020/05/15/differentiable-reasoning-over-text/ Differentiable Reasoning over TextAuthorsBhuwan DhingraPublishedMay 15, 2020 “Modern search engines not only retrieve a list of pages relevant to a query but often try to directly answer our questionsContinue readingWhat I Read: Differentiable Reasoning over Text

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

Author: Andrew Fairless