Discover d909b/ame Open Source project by Patrick Schwab (@d909b)

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Language
Python
License
MIT License
Created about 6 years ago
Updated over 1 year ago

d909b

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

cxplain

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

Python

127

perfect_match

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

Python

121

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.

Python

CovEWS

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

Python

heart_rhythm_attentive_rnn

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

Python

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.

Python

eth_dream_pd_subchallenge1

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

Python

ncore

Python

CGE-Piano

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

C++

SWPUE3-Plugin

A simple plugin pattern demonstration.

Java

SWPUE11-ChainOfResponsibility

A simple Chain Of Responsibility pattern demonstration.

Java

SWPUE5-Observer

A simple observer pattern demonstration.

C++

SWPUE7-Adapter

A simple adapter pattern demonstration.

Java

SWPUE1-Singleton

A simple singleton pattern demonstration

C++

SWPUE4-Composite

A simple composite pattern demonstration

C++

d909b/ame

d909b

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