Francesco Crecchi

Multiscale-Parametric-t-SNE. ESANN20 paper code repository. This package is a perplexity-free extension of Parametric t-SNE dimensionality reduction method implemented in `Keras` and compatible with `Scikit-learn`.

DropIn-ESN. This repository contains the code used to produce the results presented in the IJCNN 2017 paper "DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout" by D. Bacciu, F. Crecchi (University of Pisa) and D. Morelli (Biobeats LTD).

AE_Detector. Python

Intro_Keras. Introduction to Keras deep learning framework.

FADER. Fast Adversarial Examples Reject repository.

DtNR. t-SNE based Deep Neural Rejection (DNR) repository.

Classpad. Classpad