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Extension of text_explainability for sensitivity testing (robustness & fairness).
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A generic explainability architecture for explaining text machine learning models.
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vig / mscprojects / Blue Noise Distributed MCMC Decorrelation of ReSTIR
BSD 3-Clause "New" or "Revised" License2023-01 Project by Oscar Fickel and supervised by Peter Vangorp
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vig / mscprojects / Projection Explain
MIT LicenseA tool used to interact and understand dimensionality reduced data. It can be used to generate, view and explain point clouds!
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Nikos Pappas / phap
MIT LicenseA snakemake workflow that wraps various phage-host prediction tools
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BSC project done by Joep Robben on "Deep learning steerable projections using a supervised modular neural network", Supervised by Michael Behrisch
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Project 2 for Pattern Recognition about colorization of images using Neural Networks.
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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.
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Here are the code and data files placed which I used for the publication in JGR:oceans about the effect of freshwater pulses on estuarine salinity.
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