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Uncertainty-aware Personal Assistant for Making Personalized Privacy Decisions

A personal privacy assistant (PURE) helps its user make privacy decisions by recommending privacy labels (private or public) for given contents. PURE is unobtrusive, uncertainty-aware, and personalized.

In this repo, you can find how we implement PURE, Standard Neural Networks (SNN), and existing models such as Monte Carlo (MC) dropout and Deep Ensemble.

This is a demo repository!

Project by Bram Dijkers and supervised by Michael Behrisch (05/22)

(Very messy) codebase for my Master's thesis project on semi-supervised point cloud classification using label diffusion lidar segmentation

Creators: Floris Roos and Jos Zuijderwijk. Creating a classifier to choose the best modularity seeking algorithm on graphs.

BSC project done by Joep Robben on "Deep learning steerable projections using a supervised modular neural network", Supervised by Michael Behrisch

Using Graph Embeddings for Matrix Reordering

Code base of my Master's thesis.

A tool used to interact and understand dimensionality reduced data. It can be used to generate, view and explain point clouds!

This project is maintained by Dylan Kruyff, Yuncong Yu and Michael Behrisch,

Fast local pattern search in multivariate time series with query-aware locality-sensitive hashing and relevance feedback.

This is a repository that contains my solutions for Advent of Code 2021. This year, I will write everything in Python 3.