multi-layer-networks

Code used for the paper "Node and layer eigenvector centralities for multiplex networks", Tudisco, Arrigo, Gautier, SIAM J. Appl. Math 2017

Code used for the paper "Multi-dimensional HITS: An always computable ranking for temporal multi-layer directed networks", Arrigo, Tudisco, 2018

Various tasks related to graphs and networks theory

The main goal of this work is to build and train multilayer NNs, train autoencoders to reduce the number of features for the classifiers and build and train deep networks (CNN and LSTM) for predicting or detecting the seizures.

Classification is made in 2-dimensional space with artificial neural networks learning rules. Perceptron and Delta learning rules are implemented in different layers.

Lightweight multi-layer neural network library implemented in pure javascript.

Experiment with Neural Network architectures to build and evaluate both single and multi-layer sequential models in Keras

Yield curve specific (multi-layer) network analysis (incl. time evolution and Nelson-Siegel + Neural HJM residuals) with a GUI or API. For a more generic network evolution analysis of panel time series data: https://github.com/FulgentMcGuffin/tgraphportfolio.

Demonstrates how a MLP-NN can be used to classify image and sound data. Example data set consists of images / sound files of digits. We (team of 5-6 students) developed the demo for a machine learning course.

Notes, tutorials, code snippets and templates focused on NNs for Machine Learning

Analyze (evolving) networks and multi-layer networks built from panel time series data in python or the supplied GUI. Inspired by https://github.com/mayabenowitz/Hedgecraft.

Implementation of papers: Rádli, R., & Czúni, L.: Deep Randomized Networks for Fast Learning (2023)

we present a comparative study of various methodologies for processing brain network data with the aim of classifying subjects using Graph Neural Networks (GNNs). Specifically, we explore different strategies for constructing informative graph representations to distinguish people with multiple sclerosis (MS) from healthy controls.

AI-grand_challenge

Identifying Image Orientation using Supervised Machine Learning Models of k-Nearest Neighbors, Adaboost and Multi-Layer Feed-Forward Neural Network trained using Back-Propagation Learning Algorithm