Explore projects

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.

Small 3D engine visualizing a scene graph using rasterization.

This repository has moved to https://github.com/marcelrobeer/explabox. Documentation is available at https://explabox.readthedocs.io. Explore/examine/explain/expose your model with the explabox!

Example GitBook site using GitLab Pages: https://pages.gitlab.io/gitbook