Scientific Software
Updated 10 months ago

imodels — Peer-reviewed • Rank 21.0 • Science 100%

imodels: a python package for fitting interpretable models - Published in JOSS (2021)

Scientific Software
Updated 10 months ago

Ethome — Peer-reviewed • Rank 5.9 • Science 98%

Ethome: tools for machine learning of animal behavior - Published in JOSS (2024)

Engineering
Scientific Software · Peer-reviewed
Updated 10 months ago

susi • Rank 5.8 • Science 77%

SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)

Updated 10 months ago

paddlescience • Rank 15.3 • Science 67%

PaddleScience is SDK and library for developing AI-driven scientific computing applications based on PaddlePaddle.

Updated 10 months ago

hyperspectral-regression • Rank 4.4 • Science 77%

Code examples for the book chapter "Supervised, Semi-Supervised and Unsupervised Learning for Hyperspectral Regression".

Updated 10 months ago

torchtime • Rank 3.6 • Science 67%

Benchmark time series data sets for PyTorch

Updated 10 months ago

tyc-dataset • Rank 2.3 • Science 54%

Official and maintained implementation of the dataset paper "The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures" [ICCVW 2023].

Updated 10 months ago

opfgym • Science 57%

A gymnasium-compatible framework to create reinforcement learning (RL) environment for solving the optimal power flow (OPF) problem. Contains five OPF benchmark environments for comparable research.

Updated 10 months ago

temporal-link-prediction • Science 44%

Code used in `Supervised temporal link prediction in large-scale real-world networks'

Updated 10 months ago

pdll • Science 54%

Pairwise Difference Learning (PDL) is a meta-learning framework that leverages pairwise differences to transform multiclass problems into binary tasks. This repository includes the original PDL Classifier implementation, along with extended versions for regression and weighted learning scenarios.

Updated 9 months ago

imesc • Science 44%

This app is intended to dynamically integrate machine learning techniques to explore multivariate data sets.