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Massinissa's Projects

deepcca icon deepcca

An implementation of Deep Canonical Correlation Analysis (DCCA or Deep CCA) in Keras with tfv2 backend.

deepfool icon deepfool

A simple and accurate method to fool deep neural networks

esp-idf icon esp-idf

Espressif IoT Development Framework. Official development framework for ESP32.

hasca-shl icon hasca-shl

Bayesian Optimization of Neural Architectures for Human Activity Recognition

kaa icon kaa

Kaa open-source middleware platform for building, managing, and integrating connected products with the Internet of Everything.

latex-templates icon latex-templates

A collection of LaTeX templates used for research, courses, and miscellanea.

learning-group-structure icon learning-group-structure

Code associated with our paper "Learning Group Structure and Disentangled Representations of Dynamical Environments"

mraa icon mraa

Linux Library for low speed IO Communication in C with bindings for C++, Python, Node.js & Java. Supports generic io platforms, as well as Intel Edison, Intel Joule, Raspberry Pi and many more.

openthread icon openthread

OpenThread released by Nest is an open-source implementation of the Thread networking protocol

pytorch icon pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch_rvae icon pytorch_rvae

Recurrent Variational Autoencoder that generates sequential data implemented with pytorch

riot icon riot

RIOT - The friendly OS for IoT

scikit-multiflow icon scikit-multiflow

A machine learning framework for multi-output/multi-label and stream data. Inspired by MOA and MEKA, following scikit-learn's philosophy.

shl-nas icon shl-nas

Learning Mobility-Related Human Activities from Sensor-Rich Environments

simavr icon simavr

simavr is a lean, mean and hackable AVR simulator for linux & OSX

smoky icon smoky

Face Recognition Application

stanford-ctc icon stanford-ctc

Neural net code for lexicon-free speech recognition with connectionist temporal classification

tensorflow icon tensorflow

Computation using data flow graphs for scalable machine learning

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