The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
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Updated
Jun 13, 2024 - Python
The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
An explainable inference software supporting annotated, real valued, graph based and temporal logic
Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms
PyTorch code for the RetoMaton paper: "Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML 2022)
mOWL: Machine Learning library with Ontologies
Experimental Python implementation of the Clarion cognitive architecture
Codebase for VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
Codebase for the paper: Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
📜 [Under Review] "Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search", Wenqing Zheng*, S P Sharan*, Zhiwen Fan, Kevin Wang, Yihan Xi, Atlas Wang
A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
A neurosymbolic parser for Dutch.
Code for the ICLR 2024 paper "How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data"
PyTorch Lightning based TP-N2F model.
Source code for the neuro-fuzzy network of the paper: "Logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification"
The official implementation of an Insect-Inspired Randomly Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning (NeSy20/21@IJCLR)
On the Hardness of Probabilistic Neurosymbolic Learning (ICML2024)
PyTorch implementation for the Neuro-Symbolic Concept Learner (NS-CL).
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