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arff

ARFF formatted file reader in C++

avalanche

Avalanche: an End-to-End Library for Continual Learning.

brain-inspired-replay

A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).

capymoa

Enhanced machine learning library tailored for data streams, featuring a Python API integrated with MOA backend support. This unique combination empowers users to leverage a wide array of existing algorithms efficiently while fostering the development of new methodologies in both Python and Java.

catastrophic-diffusion

A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

compx556

My GitHub for handing in assignments for meta-heuristic algorithms

continual-learning-baselines

Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.

dpy-anti-spam

Ever wanted a bot to automatically deal with spammers? This is your discord.py library for it.

efficient-kan

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

gothello

Gothello is a combination of the games of "Go" and "Othello"

hydra

Hydra is a framework for elegantly configuring complex applications

jellyfin

The Free Software Media System

jellyfin-web

Web Client for Jellyfin

jpype

JPype is cross language bridge to allow Python programs full access to Java class libraries.

maelicious

mammoth

An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

moa

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

my-models

online-stability-tune

Lee, A., Gomes, D. H. M., & Zhang, D. Y. (2022). Balancing the Stability-Plasticity Dilemma with Online Stability Tuning for Continual Learning. Proceedings of 2022 International Joint Conference on Neural Networks (IJCNN)

punter

recovery_likelihood

river

🌊 Online machine learning in Python

skmf-forever

A machine learning package for streaming data in Python. The other ancestor of River.

split-and-bridge

Split-and-Bridge strategy for comparison

surprisenet-cikm-23

SurpriseNet is a class incremental continual learning technique. It allows a neural network to learn from a stream or sequence of classes rather than a traditional static dataset. The main challenge it solves is differentiating classes that were never presented side-by-side.

testrepo