ema-marconato/NeSy-CL
Codebase for Neuro-Symbolic Continual Learning.
This project helps machine learning researchers and practitioners address the challenge of "catastrophic forgetting" when developing AI models that learn new tasks sequentially. It enables building models that can continually learn by mapping raw inputs to high-level concepts and performing consistent reasoning, even when previous knowledge and concepts need to be retained. This is for AI/ML researchers, data scientists, and engineers working on intelligent systems that must adapt and evolve over time.
No commits in the last 6 months.
Use this if you are building AI systems that need to learn new skills or interpret new data over time without forgetting previously acquired knowledge or reasoning abilities.
Not ideal if your AI model learns a task once and does not need to adapt or acquire new knowledge incrementally.
Stars
27
Forks
2
Language
Python
License
MIT
Category
Last pushed
Aug 21, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ema-marconato/NeSy-CL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
aimagelab/mammoth
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of...
LAMDA-CL/PyCIL
PyCIL: A Python Toolbox for Class-Incremental Learning
GMvandeVen/continual-learning
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR,...
LAMDA-CL/LAMDA-PILOT
🎉 PILOT: A Pre-trained Model-Based Continual Learning Toolbox
mmasana/FACIL
Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.