laura-rieger

battery-life-prediction

deep-explanation-penalization

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

dimenet

DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules" (NeurIPS-W 2020)

enn

in-situ-analysis

labplot

My matplotlib style sheets

laura-rieger.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

LauraRieger.github.io

Laura Hannemose Rieger personal website

openingblackbox

Opening the Black Box How to Interpret Machine Learning Functions and Their Decisions

SpectraML-Classification

laura-rieger/deep-explanation-penalization

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

laura-rieger/openingblackbox

Opening the Black Box How to Interpret Machine Learning Functions and Their Decisions

laura-rieger/battery-life-prediction

laura-rieger/SpectraML-Classification

laura-rieger/dimenet

DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules" (NeurIPS-W 2020)

laura-rieger/enn

laura-rieger/in-situ-analysis

laura-rieger/labplot

My matplotlib style sheets

laura-rieger/laura-rieger.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

laura-rieger/LauraRieger.github.io

Laura Hannemose Rieger personal website

laura-rieger/lottery-ticket-hypothesis

A reimplementation of "The Lottery Ticket Hypothesis" (Frankle and Carbin) on MNIST.

laura-rieger/Lottery-Ticket-Hypothesis-in-Pytorch

This repository contains a Pytorch implementation of the paper "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" by Jonathan Frankle and Michael Carbin that can be easily adapted to any model/dataset.

laura-rieger/max_switch_reconstruction_tunnel