The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Link prediction, in particular, has a substantial social impact. However, link prediction algorithms tend to increase the segregation in social networks by disfavoring the links between individuals in specific demographic groups. This paper proposes a novel way to enforce fairness on graph neural networks with a fine-tuning strategy
This tutorial will show you how to create a neural network in TensorFlow. By the end of this tutorial, you will have a working TensorFlow network that can be
Skip to content Learning Machines A blog about data, science, and learning machines – like us Logistic Regression as the Smallest Possible Neural Network We already covered Neural Networks and Logistic Regression in this blog. If you want to gain an even deeper understanding of the fascinating connection between those two popular machine learning techniques read on! Let us recap what an artificial neuron looks like: Mathematically it is some kind of non-linear activation function of the scalar product of
Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent studies have suggested that convolutional neural networks (CNNs) fail to generalize to out-of-distribution (OOD) category–viewpoint combinations, that is, combinations not seen during training. Here we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and three-dimensional viewpoint on OOD combinations, and identifying the neural mechanisms
review of Spikes (see below), I won't repeat myself here. Things to try to understand: Distributed and population codes. How much can be understood about coding without also understanding computation? Things to do: Causal-state reconstruction on real neural spike data. (Done; see below.) Transducer state reconstruction; states of the inferred transducer = classes of stimuli (+ internal histories) which make a difference to the cell. The information coherence measure should indicate the quantity of distrib
Spiking Neural Networks As Universal Function Approximators
Table Of Contents - Preface - Installation - Notation - 1. Introduction - 2. Preliminaries - 2.1. Data Manipulation - 2.2. Data Preprocessing - 2.3. Linear Algebra - 2.4. Calculus - 2.5. Automatic Differentiation - 2.6. Probability and Statistics - 2.7. Documentation 3. Linear Neural Networks for Regression - 3.1. Linear Regression - 3.2. Object-Oriented Design for Implementation - 3.3. Synthetic Regression Data - 3.4. Linear Regression Implementation from Scratch - 3.5. Concise Implementation of Linea
Annotated history of modern AI and deep neural networks Jürgen Schmidhuber , KAUST AII , Swiss AI Lab IDSIA, USI Pronounce: You_again Shmidhoobuh Technical Report IDSIA-22-22 (v2), IDSIA, 12/29/2022 @SchmidhuberAI [email protected] arXiv:2212.11279 Annotated History of Modern AI and Deep Learning Abstract. Machine learning (ML) is the science of credit assignment: finding patterns in observations that predict the consequences of actions and help to improve future performance. Credit assignment is also
In my last article on graph theory, I briefly introduced my latest topic of interest: Graph Convolutional Networks. It's entirely possible
Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity December 24, 2018 Intel Neural Compute Stick 2 Filed under: Neural Information Processing , Neural Networks — Patrick Durusau @ 3:20 pm Intel Neural Compute Stick 2 (Mouser Electronics) From the webpage: Intel® Neural Compute Stick 2 is powered by the Intel™ Movidius™ X VPU to deliver industry leading performance, wattage, and power. The NEURAL COMPUTE supports OpenVINO™, a toolkit that accelerates solution development and