RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

Last update: Dec 12, 2022

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Deep Learning ripsnet

Overview

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

This repository contains the code asscoiated to this arXiv article.

tutorial.ipynb is a jupyter-notebook to run a small version of our experiments on a synthetic data set. It has been tested with Tensorflow version 2.5.0.
expes_synth_ucr.zip contains code and instructions for reproducing the full experiments on synthetic and UCR time series data sets.
expes_3Dshape.zip contains code and instructions for reproducing the full experiments on 3D shape data sets.

Owner

Felix Hensel

Postdoc in Machine Learning and TDA PhD in Mathematics

GitHub Repository

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RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

Related tags

Overview

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

Owner

Felix Hensel

Turning SymPy expressions into PyTorch modules.

Sleep staging from ECG, assisted with EEG

[ICCV 2021] Code release for "Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks"

The official TensorFlow implementation of the paper Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition

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StocksMA is a package to facilitate access to financial and economic data of Moroccan stocks.

Deep Networks with Recurrent Layer Aggregation

《Towards High Fidelity Face Relighting with Realistic Shadows》(CVPR 2021)

TrackTech: Real-time tracking of subjects and objects on multiple cameras

Simulating Sycamore quantum circuits classically using tensor network algorithm.

Code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search.

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