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.
Active learning based workflow for automated 3D-EM segmentation
Images, Active learning
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
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
Library for semi-supervised clustering using pairwise constraints
Time series, Active learning, Anomaly detection, Un/Semi-supervised learning
A Python-based deep learning framework for multi-target prediction
Supervised learning, Recommender systems
Deep Learning and Probabilistic Logic Programming
Probabilistic programming, Networks & Graphs, Supervised learning, Relational databases, Images
Time series distances (DTW, ED), clustering and subsequence search
Time series, Anomaly detection, Un/Semi-supervised learning
