neural network – Page 25 – Andrew Fairless, Ph.D.

What I Read: Can Neural Networks Show Imagination?

By Andrew Fairless on January 24, 2021December 30, 2020

https://medium.com/dataseries/can-neural-networks-show-imagination-deepmind-thinks-they-can-b3b874aece67 Can Neural Networks Show Imagination? DeepMind Thinks they CanDeepMind has done some of the relevant work in the area of simulating imagination in deep learning systems.Jesus RodriguezSep 14 “IncorporatingContinue readingWhat I Read: Can Neural Networks Show Imagination?

What I Read: Traffic prediction with Graph Neural Networks

By Andrew Fairless on January 23, 2021December 30, 2020

https://deepmind.com/blog/article/traffic-prediction-with-advanced-graph-neural-networks Traffic prediction with advanced Graph Neural Networks “We discovered that Graph Neural Networks are particularly sensitive to changes in the training curriculum… the most successful approach to this problemContinue readingWhat I Read: Traffic prediction with Graph Neural Networks

What I Read: Continual Learning… and the Bridge to Active and Open World Learning

By Andrew Fairless on January 22, 2021December 30, 2020

https://arxiv.org/abs/2009.01797 A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World LearningMartin Mundt, Yong Won Hong, Iuliia Pliushch, Visvanathan RameshContinue readingWhat I Read: Continual Learning… and the Bridge to Active and Open World Learning

What I Read: Transformer Architecture

By Andrew Fairless on January 21, 2021December 29, 2020

https://blog.exxactcorp.com/a-deep-dive-into-the-transformer-architecture-the-development-of-transformer-models/ Deep LearningA Deep Dive Into the Transformer Architecture – The Development of Transformer ModelsMarketing, July 14, 2020 0 11 min readTransformers for Natural Language Processing “There’s no better timeContinue readingWhat I Read: Transformer Architecture

What I Read: Floating-Point and Deep Learning

By Andrew Fairless on January 18, 2021December 27, 2020

https://eigenfoo.xyz/floating-point-deep-learning/ Floating-Point Formats and Deep Learning “Floating-point format is not a crucial consideration in deep learning, but it can make a significant difference. What is floating-point, why should you (aContinue readingWhat I Read: Floating-Point and Deep Learning

What I Read: The Cost of AI Training is Improving

By Andrew Fairless on January 9, 2021December 23, 2020

https://ark-invest.com/analyst-research/ai-training/ The Cost of AI Training is Improving at 50x the Speed of Moore’s Law: Why It’s Still Early Days for AIJames Wang-Work-Remote-Headshotby James Wang, Analyst May 06, 2020 “InContinue readingWhat I Read: The Cost of AI Training is Improving

What I Read: Progress of Natural Language Processing

By Andrew Fairless on January 5, 2021December 22, 2020

https://blog.exxactcorp.com/the-unreasonable-progress-of-deep-neural-networks-in-natural-language-processing-nlp/ Deep LearningThe Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)Marketing, June 2, 2020 0 14 min read “With the advent of pre-trained generalized language models, weContinue readingWhat I Read: Progress of Natural Language Processing

What I Read: making machine learning actually useful

By Andrew Fairless on January 5, 2021December 22, 2020

https://www.shreya-shankar.com/making-ml-work/ Shreya ShankarReflecting on a year of making machine learning actually usefulJune 29, 2020 in #machine learning · 11 min read “…ML research as a field puts too much emphasisContinue readingWhat I Read: making machine learning actually useful

What I Read: Symbolic Models from Deep Learning

By Andrew Fairless on January 4, 2021December 22, 2020

https://astroautomata.com/paper/symbolic-neural-nets/ Discovering Symbolic Models from Deep Learning with Inductive BiasesPaper Code Interactive NotebookPaper authors: Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho “…when doesContinue readingWhat I Read: Symbolic Models from Deep Learning

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

Tag: neural network