The Identity-aware Graph Neural Networks (ID-GNNs) framework enhances the expressive power of message-passing GNNs beyond the 1-Weisfeiler-Lehman test by inductively injecting identity information
# NeuralProphet: A Time-Series Modeling Python Library based on Neural-Networks Machine Learning Neural-Network Python Library Time Series Analysis Tutorial Author Esmaeil Alizadeh Published December 3, 2020 ## On this page - NeuralProphet Library - NeuralProphet vs. Prophet - Project Maintainers - Installation - Implementation with a Case Study Note 👉 This article is also published on Towards Data Science blog . NeuralProphet 1 is a python library for modeling time-series data based on neura
# The Problem of Reproducability in Neural Networks Published 2022-12-01 by Kevin Feasel Pete Warden explains a problem : Last week I had a question from a colleague about reproducibility in TensorFlow, specifically in the 1.14 era. He wanted to be able to run the same training code multiple times and get exactly the same results, which on the surface doesn’t seem like an unreasonable expectation. Machine learning training is fundamentally a series of arithmetic operations applied repeatedly, so what mak
Learn what is deep learning and how neural networks actually work. Simple explanations of layers, training, and why deep learning powers today's best AI
Abstract page for arXiv paper 2405.15868v2: LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
Computing the expectation of the description length for neural networks
Distributed deep neural networks over the cloud, the edge, and end devices Teerapittayanon et al., ICDCS 17 Earlier this year we looked at Neurosurgeon, in which the authors do a brilliant job of exploring the trade-offs when splitting a DNN such that some layers are processed on an edge device (e.g., mobile phone), and some
Combining neural networks with a memory system allows for human-like learning of general algorithms. Read on to find out more
An attempt at a visual explanation of convolutions and the basic philosophy behind convolutional neural networks (CNNs
In humans and other mammals, the cerebral cortex is responsible for sensory, motor, and cognitive functions. Understanding the organization of the neuronal networks in the cortex should provide insights into the computations that they carry out. A study publishing on July 21st in open access journal PLOS Biology shows that the global architecture of the cortical networks in primates (with large brains) and rodents (with small brains) is organized by common principles. Despite the overall network invariances