Riccardo Cadei (riccardocadei)

riccardocadei

Geek Repo

Company:EPFL

Location:Lausanne

Home Page:https://www.riccardocadei.com/

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Riccardo Cadei's repositories

photovoltaic-detection

Detecting available rooftop area from satellite images to install photovoltaic panels - LESO-PB Lab @ EPFL

Language:Jupyter NotebookLicense:MITStargazers:15Issues:0Issues:0

Higgs-Boson-Challange-2020-EPFL

A classification problem on a big physical dataset simulated by the ATLAS experiment from CERN - @ EPFL

Language:Jupyter NotebookLicense:MITStargazers:6Issues:1Issues:0

Geo-Area-Classifier

Classification of satellite images among Residential, Industrial and No Available Rooftop Area class using Deep Learning - LESO-PB Lab @ EPFL

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deeplearning-framework

Mini Deep Learning framework to create and train simple Neural Network (i.e. Multi Layer Perceptron) through Back-Propagation Algorithm using only PyTorch's tensor definition and elementary mathematical operations.

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Robust-Journey-Planning

Robust and Efficient Journey Planning for Switzerland - @ EPFL

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adahessian

Discussion of advantages and disadvantages of AdaHessian, a state-of-the-art Second Order Methods over First Order Methods on a Non-Convex Optimization Problem (digits classification on MNIST database using ResNet18). - @ EPFL

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LunarLander-v2-REINFORCE

Teaching to an agent to play the Lunar Lander game from OpenAI Gym using REINFORCE.

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ML4Networking-Contest-2019-PoliMI

Customer churn classification - @ Politecnico di Milano

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digits-comparison

Comparing couple of images from MNIST dataset using DCNN.

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GraphML-Contest-2019-Oracle-PoliMI

Multi‑label vertex classification on a protein‑protein interaction network generating a powerful embedding of its graph - @ Oracle Labs, Politecnico di Milano

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Metropolis-Hastings-for-optimization

Deploying a 5G Network in a country

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pneumonia-diagnosis

A self-supervised learning at test time approach for robust and adaptable diagnosis of pneumonia on chest X-rays data.

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riccardocadei

Introduction

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riccardocadei.github.io

Personal Portfolio

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Trajectron-plus-plus

Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).

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