Pinned Repositories
MIDL24-segmentation_quality_control
Experiments for MIDL 2024 Submission: Controlling Segmentation Quality per Image
bachelor_thesis
My Bachelor Thesis on Recursive Partitioning within a Conditional Inference Framework. Written in 2018 at the Faculty of Statistics, Technical University Dortmund
Covid_Incidence_Biases
Points out misleading biases in the Covid Incidence statistic which was heavily used by the German Government to guide lockdown decisions. Conducted as practical module at University of Tuebingen.
csvae4counterfactuals
Seminar paper which presents the CS-VAE model (Klys et al., 2018) in the context of Counterfactual Recourse. Provides a walk-through and rationale for deriving the rather complicated loss function. Code for training a vanilla VAE on MNIST.
Microbiome_Embeddings
A comparison between novel linear and nonlinear embedding methods for data from the American Gut Project. Project conducted as a student of M.Sc. Machine Learning at University of Tuebingen.
perf-pred-sandbox
Exploring Methods for Performance Prediction and their Uncertainty Quantification
pat-rig's Repositories
pat-rig/bachelor_thesis
My Bachelor Thesis on Recursive Partitioning within a Conditional Inference Framework. Written in 2018 at the Faculty of Statistics, Technical University Dortmund
pat-rig/Covid_Incidence_Biases
Points out misleading biases in the Covid Incidence statistic which was heavily used by the German Government to guide lockdown decisions. Conducted as practical module at University of Tuebingen.
pat-rig/csvae4counterfactuals
Seminar paper which presents the CS-VAE model (Klys et al., 2018) in the context of Counterfactual Recourse. Provides a walk-through and rationale for deriving the rather complicated loss function. Code for training a vanilla VAE on MNIST.
pat-rig/Microbiome_Embeddings
A comparison between novel linear and nonlinear embedding methods for data from the American Gut Project. Project conducted as a student of M.Sc. Machine Learning at University of Tuebingen.
pat-rig/perf-pred-sandbox
Exploring Methods for Performance Prediction and their Uncertainty Quantification