FilippoMB/Ensemble-Conformalized-Quantile-Regression

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FilippoMB/Ensemble-Conformalized-Quantile-Regression

FilippoMB

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Valid and adaptive prediction intervals for probabilistic time series forecasting

Time-series-classification-and-clustering-with-Reservoir-Computing

Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling

Experimental results obtained with the MinCutPool layer as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling"

python-time-series-handbook

Material for the course "Time series analysis with Python"

Jupyter Notebook

Diffusion_models_tutorial

Jupyter Notebook

Benchmark_dataset_for_graph_classification

This repository contains a dataset for testing graph classification algorithms, such as Graph Kernels and Graph Neural Networks.

The-expressive-power-of-pooling-in-GNNs

Code and dataset to test empirically the expressive power of graph pooling operators.

Simplifying-Clustering-with-Graph-Neural-Networks

Tensorflow and Pytorch implementation of "Just Balance GNN" for graph clustering.

Bidirectional-Deep-readout-Echo-State-Network

Multi-variate time series classification with a bi-directional ESN with a readout implemented as a deep neural network.

Total-variation-graph-neural-networks

Pytorch and Tensorflow implementation of TVGNN, presented at ICML 2023.

TCK_AE

Physics-Informed-Neural-Networks-tutorial

Hands-on tutorial for implementing Physics Informed Neural Networks in Pytorch

Jupyter Notebook

Variational-Graph-Auto-encoders-Tensorflow-2-Spektral-

Pyramidal-Reservoir-Graph-Neural-Networks

Software implementation of Pyramidal Reservoir Graph Neural Networks

Time-Series-Cluster-Kernel

Kernel similarity for classification and clustering of multi-variate time series with missing values

Jupyter Notebook

Learning-representations-for-multivariate-time-series-with-missing-data

Encoding of real valued time series with missing data with an autoencoder regularized with TCK

lecture_RNN_phase_space

Jupyter Notebook

Autoencoder-Tensorflow

Autoencoer in Tensorflow with most of the basic functionalities and options for training. Multiple models can be handled and trained at the same time.

Tutorial_GNN_explainability

Jupyter Notebook

Recognition-of-polar-lows-in-Sentinel-1-SAR-images-with-deep-learning

Code implementation for the paper "Recognition of polar lows in Sentinel-1 SAR images with deep learning"

Jupyter Notebook