Toolbox-lijst

Een overzicht van de  "AI toolboxes" gerelateerd aan of ontwikkeld met de steun van het Vlaams AI Onderzoeksprogramma.

Deze lijst is uitsluitend informatief. De eigenaar van de betreffende toolbox is verantwoordelijk voor de inhoud daarvan. Voor de gebruiksvoorwaarden, zie de individuele toolboxes. De partners van het Vlaams AI-Onderzoeksprogramma geven geen garanties met betrekking tot deze toolboxes, zoals maar niet beperkt tot de kwaliteit, toegankelijkheid en toepasbaarheid van de tools. 

Tip: Je kunt filteren op de toolboxes per categorie - zie filter boven de tabel

Titel
Omschrijving
Categorieën
Link

Multiobjective Ranking and Selection Using Stochastic Kriging

Optimization

Multi-objective hyperparameter optimization with performance uncertainty

Optimization

Adhesive Bonding Optimization Toolbox with Uncertainty and Feasibility constraints

Optimization

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

Overzicht van de Toolboxes per categorie

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