Data and code for the paper "Importance of Kernel Bandwidth in Quantum Machine Learning"

Last update: Oct 23, 2022

Overview

Reproducibility materials for "Importance of Kernel Bandwidth in Quantum Machine Learning"

Repo structure:

code contains Python scripts used to generate the data
reproduce-figures contains Jupyter notebooks that generate the figures in the paper
data contains the datasets used

Requirements

Easiest way to reproduce my set up is to install anaconda3 and run

conda env create -f environment.yml

Obtaining raw data

Full raw data can be downloaded here (~23GB): https://web.cels.anl.gov/~rshaydulin/results.zip

To reproduce figures, you have to first download the data (results.zip) and unzip it into data folder.

Owner

Ruslan Shaydulin

GitHub Repository

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Data and code for the paper "Importance of Kernel Bandwidth in Quantum Machine Learning"

Related tags

Overview

Reproducibility materials for "Importance of Kernel Bandwidth in Quantum Machine Learning"

Requirements

Obtaining raw data

Owner

Ruslan Shaydulin

Experiments on continual learning from a stream of pretrained models.

Live training loss plot in Jupyter Notebook for Keras, PyTorch and others

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Codes for TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.

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