Toolbox List

An overview of  "AI toolboxes" linked to or developed with support of the Flanders AI Research Program.   

The list is provided for information purposes only. The owner of the toolbox is responsible for the content thereof. For conditions of use, check the individual toolboxes. The partners of the Flanders AI Program make no warranty of any kind with regard to these toolboxes, including but not limited to the quality, accessibility or applicability of the tools.

Tip: You can filter on the toolboxes per category - see filter on top of the table.


Multiobjective Ranking and Selection Using Stochastic Kriging


Multi-objective hyperparameter optimization with performance uncertainty


Adhesive Bonding Optimization Toolbox with Uncertainty and Feasibility constraints


Code for calibration of trained segmentation models using platt scaling, fine tuning and simple convolutional auxiliary networks 

Images, Supervised learning, Optimization

Long Short-term Cognitive Network (LSTCN) model for time series forecasting

Time series

DeepStochLog is a neuro-symbolic framework that combines grammars, logic, probabilities and neural networks. 

Probabilistic programming

Overview of Toolboxes per category

Bootstrapped Dual Policy Iteration: sample-efficient model-free RL with discrete actions

Reinforcement learning

A Python package for fairness metrics in language models

Text, Natural language processing

Active learning based workflow for automated 3D-EM segmentation

Images, Active learning

A software for evaluating model calibration, volume bias and a correlation between the two

Supervised learning, Images

A procedure for calculating confidence intervals for the eigendecomposition of covariance matrices.

Un/Semi-supervised learning

Solver for executing constraint Decision Model and Notation (cDMN) models.

Symbolic AI

‘cEASEr’ and ‘Add-EASEr’ algorithms, as presented in the publication ‘Closed-Form Models for Collaborative Filtering with Side-Information’

Recommender systems

Library for unsupervised clustering of  large data sets of T cell receptor sequences.

Un/Semi-supervised learning, DNA sequence analysis