machine learning – Page 34 – Andrew Fairless, Ph.D.

What I Read: Learning Neural Network Subspaces

By Andrew Fairless on September 20, 2021September 4, 2021

https://arxiv.org/abs/2102.10472 Learning Neural Network SubspacesMitchell Wortsman, Maxwell Horton, Carlos Guestrin, Ali Farhadi, Mohammad Rastegari “These neural network subspaces contain diverse solutions that can be ensembled, approaching the ensemble performance ofContinue readingWhat I Read: Learning Neural Network Subspaces

What I Read: Graph Theory Into New Dimensions

By Andrew Fairless on September 16, 2021September 4, 2021

https://www.quantamagazine.org/how-big-data-carried-graph-theory-into-new-dimensions-20210819/ How Big Data Carried Graph Theory Into New DimensionsStephen OrnesContributing WriterAugust 19, 2021 ““Now we know that the network is just the shadow of the thing,”… If a dataContinue readingWhat I Read: Graph Theory Into New Dimensions

What I Read: Machines Can Learn, Can They Unlearn?

By Andrew Fairless on September 15, 2021August 20, 2021

https://www.wired.com/story/machines-can-learn-can-they-unlearn/ Now That Machines Can Learn, Can They Unlearn?Privacy concerns about AI systems are growing. So researchers are testing whether they can remove sensitive data without retraining the system fromContinue readingWhat I Read: Machines Can Learn, Can They Unlearn?

What I Read: Systems for Machine Learning

By Andrew Fairless on September 14, 2021August 20, 2021

https://thegradient.pub/systems-for-machine-learning/ Systems for Machine LearningKabir Nagrecha14.Aug.2021 “Machine learning’s increasing importance to real-world applications brought awareness of a new field focused on ML in practice – machine learning systems (or, asContinue readingWhat I Read: Systems for Machine Learning

What I Read: XGBoost, Order Does Matter

By Andrew Fairless on September 13, 2021August 20, 2021

https://towardsdatascience.com/xgboost-order-does-matter-60d8d0d5aa71?gi=d051128bfa6b XGBoost: Order Does MatterWhat impact does the order of the features have on XGBoost models, and how can you use feature importance to identify related features in your data?BityaContinue readingWhat I Read: XGBoost, Order Does Matter

What I Read: Machine Learning, Rendezvous Architecture

By Andrew Fairless on September 8, 2021August 17, 2021

https://www.saigeetha.in/post/machine-learning-rendezvous-architecture Machine Learning – Rendezvous ArchitectureSai Geetha M N “The Rendezvous architecture proposed by Ted Dunning and Ellen Friedman in their book on Machine Learning Logistics was a wonderful solutionContinue readingWhat I Read: Machine Learning, Rendezvous Architecture

What I Read: Computer Scientists Discover Limits of Major Research Algorithm

By Andrew Fairless on September 7, 2021August 17, 2021

https://www.quantamagazine.org/computer-scientists-discover-limits-of-major-research-algorithm-20210817/ Computer Scientists Discover Limits of Major Research AlgorithmNick ThiemeAugust 17, 2021 “Many aspects of modern applied research rely on a crucial algorithm called gradient descent…. researchers have never fullyContinue readingWhat I Read: Computer Scientists Discover Limits of Major Research Algorithm

What I Read: Machine Learning Won’t Solve Natural Language Understanding

By Andrew Fairless on September 6, 2021August 17, 2021

https://thegradient.pub/machine-learning-wont-solve-the-natural-language-understanding-challenge/ Machine Learning Won’t Solve Natural Language UnderstandingWalid S. Saba07.Aug.2021 “While NLP (Natural Language Processing) and NLU (Natural Language Understanding) are often used interchangeably, there is a substantial difference betweenContinue readingWhat I Read: Machine Learning Won’t Solve Natural Language Understanding

What I Read: Advances in TF-Ranking

By Andrew Fairless on August 31, 2021August 17, 2021

https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-RankingTuesday, July 27, 2021 Posted by Michael Bendersky and Xuanhui Wang, Software Engineers, Google Research“LTR models — unlike standard classification models that classify one item at aContinue readingWhat I Read: Advances in TF-Ranking

What I Read: demystifying graph deep learning

By Andrew Fairless on August 30, 2021August 17, 2021

https://ericmjl.github.io/essays-on-data-science/machine-learning/graph-nets/ An attempt at demystifying graph deep learningEric Ma “There are a ton of great explainers of what graph neural networks are. However, I find that a lot of themContinue readingWhat I Read: demystifying graph deep learning

Tag: machine learning