Abstract page for arXiv paper 1910.12478: Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Weight sharing ties neural network parameters to identical values, enhancing efficiency, compression, and generalization for diverse architectures
Researchers propose Random Label Prediction Heads (RLP-heads) to empirically study memorization in deep neural networks. By attaching auxiliary heads that predict random labels from intermediate representations, they estimate Rademacher complexity and analyze how memorization capacity evolves across
Low-rank extended Kalman filtering for online learning of neural networks from streaming dataPeter G. Chang, Gerardo Durán-Martín, Alex Shestopalof
This page gives you access to the Précis as well as select chapters from the book-in-progress, Statistical Mechanics, Neural Networks, and Artificial Intelligence. Chapter 9 shows how the equations underlying the Hopfield network and the (restricted) Boltzmann machine, respectively, indicate their architectures and thus their capabilities. The overview is in the Précis, and Chapter 9
大トロ ml ・ design Recurrent Neural Network Tutorial for Artists January 1, 2017 This post is not meant to be a comprehensive overview of recurrent neural networks. It is intended for readers without any machine learning background. The goal is to show artists and designers how to use a pre-trained neural network to produce interactive digital works using simple Javascript and p5.js library. Introduction Handwriting Generation with Javascript Machine learning has become a popular tool for the creative
A very very brief introduction to neural network for physicists
# Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks Hyeonseob Nam, Hyo-Eun Kim Real-world image recognition is often challenged by the variability of visual styles including object textures, lighting conditions, filter effects, etc. Although these variations have been deemed to be implicitly handled by more training data and deeper networks, recent advances in image style transfer suggest that it is also possible to explicitly manipulate the style information. Extending this idea
A network with more parameters than training examples can memorize random labels perfectly. The same network, on real data, generalizes. Classical learning…
Learn how Deep Learning models make their predictions