Normalizing flows for neuro-symbolic AI
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Updated
Aug 21, 2024 - Python
Normalizing flows for neuro-symbolic AI
PyEDCR is a package providing error detecting and corrective rules into Python. Given a model, PyEDCR's goal is to recognize when it is incorrect based on a set of conditions and then correct the incorrect prediction.
Holographic Reduced Representations
ReOnto is a neuro-symbolic approach where we use ontologies and GNNs to extract relations from sentences.
Category-theoretic representation of ALC theories
Neuro-symbolic anomaly detection and complex fault diagnosis exemplified in the automotive domain.
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
Codebase for BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts.
A `Neural = Symbolic` framework for sound and complete weighted real-value logic
Codebase for the paper "Generalized Reasoning with Graph Neural Networks by Relational Bayesian Network Encodings"
official repo for the NeurIPS 2022 paper "A Solver-Free Framework for Scalable Learning in Neural ILP Architectures"
A neural network model builder, leveraging a neuro-symbolic interface.
Sem@K: Is my knowledge graph embedding model semantic-aware?
Neuro-Symbolic Reinforcement Learning: Logical Optimal Action (LOA), a novel RL with Logical Neural Network (LNN) on text-based games
python package for fast solver-free training of neural ILP architectures
Demo for Neuro-Symbolic Agent (LOA)
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