calebgeniesse

joypy

Joyplots in Python with matplotlib & pandas :chart_with_upwards_trend:

TopoModelX

Topological Deep Learning

TopoEmbedX

Representation Learning on Topological Domains

TopoNetX

Computing on Topological Domains

Decorated-Merge-Trees

Tools for generating and comparing Decorated Merge Trees, enriched persistence-based topological data descriptors.

reciprocal_isomap

A reciprocal variant of Isomap for robust non-linear dimensionality reduction in Python

pyevtk

PyEVTK (Python Export VTK) exports data to binary VTK files for visualization/analysis with packages like Paraview, VisIt, and Mayavi.

neuraloperators-TL-scaling

Code for Characterizing Scaling and Transfer Learning Behavior of FNO in SciML

reeber

a library for shared- and distributed-memory parallel computation of merge trees

learning-subspaces

Generalization_metrics_for_NLP

[KDD 2023] code for "Test accuracy vs. generalization gap: model selection in NLP without accessing training or testing data" https://arxiv.org/pdf/2202.02842.pdf

WeightWatcher

The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

scikit-llm

Seamlessly integrate LLMs into scikit-learn.

loss-landscape

Code for visualizing the loss landscape of neural nets

matplotx

:bar_chart: More styles and useful extensions for Matplotlib

mplcyberpunk

"Cyberpunk style" for matplotlib plots

mappertools

Various helper functions for doing Mapper analysis (mainly with kmapper)

mapper_tutorial

For teaching a course in Mapper

V-Mapper

V-Mapper -

caffeine-mapper

rest2vec

rest2vec: Vectorizing the resting-state functional connectome using graph embedding

coding-interview-university

A complete computer science study plan to become a software engineer.

giotto-deep

Deep learning made topological.

pcluster

A partial clustering wrapper for Mapper

landmark_cover

A landmark-based cover for KeplerMapper

neumapper

A scalable Mapper algorithm for neuroimaging data analysis

dyneusr

Dynamical Neuroimaging Spatiotemporal Representations (DyNeuSR)

apricot

apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html

hbnm

Hierarchical brain network model (Demirtas et al., 2019)

pytorch_cluster

PyTorch Extension Library of Optimized Graph Cluster Algorithms