machine learning – Page 45 – Andrew Fairless, Ph.D.

What I Read: Mismatches between Optimization Analyses and Deep Learning

By Andrew Fairless on February 13, 2021January 14, 2021

http://www.offconvex.org/2020/10/21/intrinsicLR/ Mismatches between Traditional Optimization Analyses and Modern Deep LearningZhiyuan Li and Sanjeev AroraOct 21, 2020 “You may remember our previous blog post showing that it is possible to doContinue readingWhat I Read: Mismatches between Optimization Analyses and Deep Learning

What I Read: US Government Will Pay Doctors to Use AI Algorithms

By Andrew Fairless on February 13, 2021January 14, 2021

https://www.wired.com/story/us-government-pay-doctors-use-ai-algorithms/ Tom SimoniteBusiness11.10.2020 07:00 AMThe US Government Will Pay Doctors to Use These AI AlgorithmsThe artificial intelligence programs can diagnose eye disease in diabetics and complications in stroke patients. “TheContinue readingWhat I Read: US Government Will Pay Doctors to Use AI Algorithms

What I Read: Causal Reasoning in Probability Trees

By Andrew Fairless on February 11, 2021January 13, 2021

https://arxiv.org/abs/2010.12237 Algorithms for Causal Reasoning in Probability TreesTim Genewein, Tom McGrath, Grégoire Déletang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega “Probability trees are one of the simplest modelsContinue readingWhat I Read: Causal Reasoning in Probability Trees

What I Read: Comparing Data Version Control Tools – 2020

By Andrew Fairless on February 9, 2021January 12, 2021

https://dagshub.com/blog/data-version-control-tools/ Comparing Data Version Control Tools – 2020Oct 31, 2020 “Managing and creating the data sets used for these models requires lots of time and space, and can quickly becomeContinue readingWhat I Read: Comparing Data Version Control Tools – 2020

What I Read: Frameworks Scaling Deep Learning Training

By Andrew Fairless on February 9, 2021January 12, 2021

https://medium.com/dataseries/microsoft-and-google-open-sourced-these-frameworks-based-on-their-work-scaling-deep-learning-c0510e907038 Microsoft and Google Open Sourced These Frameworks Based on Their Work Scaling Deep Learning TrainingGoogle and Microsoft have recently released new frameworks for distributed deep learning training.Jesus RodriguezOct 26Continue readingWhat I Read: Frameworks Scaling Deep Learning Training

What I Read: Attention with Performers

By Andrew Fairless on February 7, 2021January 11, 2021

https://ai.googleblog.com/2020/10/rethinking-attention-with-performers.html Rethinking Attention with PerformersFriday, October 23, 2020Posted by Krzysztof Choromanski and Lucy Colwell, Research Scientists, Google Research “To resolve these issues, we introduce the Performer, a Transformer architecture withContinue readingWhat I Read: Attention with Performers

What I Read: Reproducing Deep Double Descent

By Andrew Fairless on February 7, 2021January 11, 2021

https://hippocampus-garden.com/double_descent/ Reproducing Deep Double DescentShion HondaJune 13, 2020 “…you’ve probably heard of bias-variance trade-off… But, do you know that there is a continuation of the U-shaped test risk curve? Surprisingly,Continue readingWhat I Read: Reproducing Deep Double Descent

What I Read: Deep Double Descent: Where Bigger Models and More Data Hurt

By Andrew Fairless on February 6, 2021January 11, 2021

https://arxiv.org/abs/1912.02292 Deep Double Descent: Where Bigger Models and More Data HurtPreetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever “We show that a variety of modern deepContinue readingWhat I Read: Deep Double Descent: Where Bigger Models and More Data Hurt

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: 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

Tag: machine learning