convolutional neural network – Page 2

What I Read: AI Researchers Fight Noise by Turning to Biology

By Andrew Fairless on February 1, 2022December 12, 2021

https://www.quantamagazine.org/ai-researchers-fight-noise-by-turning-to-biology-20211207/ AI Researchers Fight Noise by Turning to BiologyAllison WhittenContributing WriterDecember 7, 2021 “Tiny amounts of artificial noise can fool neural networks, but not humans. Some researchers are looking toContinue readingWhat I Read: AI Researchers Fight Noise by Turning to Biology

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: 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

What I Read: Not Optimized By Jax, PyTorch, or Tensorflow

By Andrew Fairless on August 24, 2021August 17, 2021

Useful Algorithms That Are Not Optimized By Jax, PyTorch, or Tensorflow Useful Algorithms That Are Not Optimized By Jax, PyTorch, or TensorflowJuly 21 2021Christopher Rackauckas “It turns out that forContinue readingWhat I Read: Not Optimized By Jax, PyTorch, or Tensorflow

What I Read: Geometric, Deep Learning

By Andrew Fairless on August 16, 2021July 22, 2021

https://towardsdatascience.com/geometric-foundations-of-deep-learning-94cdd45b451d?gi=d7fa7307aa63 Geometric foundations of Deep LearningGeometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. These principlesContinue readingWhat I Read: Geometric, Deep Learning

What I Read: Same or Different? The Question Flummoxes Neural Networks

By Andrew Fairless on July 19, 2021July 2, 2021

https://www.quantamagazine.org/same-or-different-ai-cant-tell-20210623/ Same or Different? The Question Flummoxes Neural Networks.John PavlusContributing WriterJune 23, 2021 “One of the most powerful classes of artificial intelligence systems, known as convolutional neural networks or CNNs…Continue readingWhat I Read: Same or Different? The Question Flummoxes Neural Networks

What I Read: Do Wide and Deep Networks Learn the Same Things?

By Andrew Fairless on May 28, 2021May 14, 2021

https://ai.googleblog.com/2021/05/do-wide-and-deep-networks-learn-same.html Do Wide and Deep Networks Learn the Same Things?Tuesday, May 4, 2021Posted by Thao Nguyen, AI Resident, Google Research

What I Read: Weight Banding

By Andrew Fairless on May 5, 2021April 10, 2021

https://distill.pub/2020/circuits/weight-banding/ Weight BandingMichael PetrovChelsea VossLudwig SchubertNick CammarataGabriel GohChris OlahApril 8, 2021DOI 10.23915/distill.00024.009 “Open up any ImageNet conv net and look at the weights in the last layer. You’ll find aContinue readingWhat I Read: Weight Banding

What I Read: Branch Specialization

By Andrew Fairless on May 4, 2021April 10, 2021

https://distill.pub/2020/circuits/branch-specialization/ Branch SpecializationChelsea VossGabriel GohNick CammarataMichael PetrovLudwig SchubertChris OlahApril 5, 2021DOI 10.23915/distill.00024.008 “Branch specialization occurs when neural network layers are split up into branches. The neurons and circuits tend toContinue readingWhat I Read: Branch Specialization

What I Read: Visualizing Weights

By Andrew Fairless on May 3, 2021April 10, 2021

https://distill.pub/2020/circuits/visualizing-weights/ Visualizing WeightsChelsea VossNick CammarataGabriel GohMichael PetrovLudwig SchubertBen EganSwee Kiat LimChris OlahFeb. 4, 2021DOI 10.23915/distill.00024.007 “The problem of understanding a neural network is a little bit like reverse engineering aContinue readingWhat I Read: Visualizing Weights

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