Patrick Schwab

perfect_match. ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.

cxplain. Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

drnet. 💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatments from observational data using neural networks.

ame. 🤖🤖 Attentive Mixtures of Experts (AMEs) are neural network models that learn to output both accurate predictions and estimates of feature importance for individual samples.

CovEWS. The COVID-19 Early Warning System (CovEWS) is a real-time early warning system for assessing individual COVID-19 related mortality risk.

DSMT-Nets. 👓📡 Distantly Supervised Multitask Networks (DSMT-Nets) are a deep-learning approach to semi-supervised learning that utilises distant supervision through many auxiliary tasks.

heart_rhythm_attentive_rnn. ❤️📱 Heart rhythm classification from mobile event recorder data using attentive neural networks.

eth_dream_pd_subchallenge1. 🚶📱 A deep-learning approach to automatically extract digital biomarkers for Parkinson's disease from smartphone accelerometers.

ncore. Python

CGE-Piano. A piano scene for the computer graphics class project, using OpenGL, OpenAL, assimp and GLFW.

SWPUE1-Singleton. A simple singleton pattern demonstration

SWPUE7-Adapter. A simple adapter pattern demonstration.

SWPUE5-Observer. A simple observer pattern demonstration.

SWPUE3-Plugin. A simple plugin pattern demonstration.

SWPUE11-ChainOfResponsibility. A simple Chain Of Responsibility pattern demonstration.

SWPUE4-Composite. A simple composite pattern demonstration