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Software design principles for machine learning applications
pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation
Learning function operators with neural networks.
A library for calibrating classifiers and computing calibration metrics
Normalizing flows for neuro-symbolic AI
Repository of the Tranferlab Practical Anomaly Detection workshop
Repository for the main part of the Machine Learning Control Training https://aai-institute.github.io/tfl-training-machine-learning-control
Code for the submission to the ML Reproducibility Challenge 2022, reproducing "If you like Shapley then you'll love the core"
Fork of ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
The pyDVL slides for pyData Berlin 2024
TeXmacs plugin for TransferLab contributions
Experiments for the paper "Class-wise and reduced calibration methods", ICMLA 2022
Code for the reproduction of Class-wise Shapley paper from Schoch, Xu, Ji [2022].
Repository of the appliedAI Institute TransferLab training "Simulation-Based Inference"
An elegant PyTorch deep reinforcement learning library.
TfL course on probabilistic model checking using storm
Repository with material for the RL workshop at TUM.AI
A fork of the anomalib library for research purposes
Domain specific language for configuration spaces in Python/Cython. Useful for hyperparameter optimization and algorithm configuration.