RelNN is a novel first-order deep neural model for relational learning.
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Updated
Nov 15, 2017 - Java
RelNN is a novel first-order deep neural model for relational learning.
Implementation for the Neural Logic Machines (NLM).
Tree Stack Memory Units
Pytorch implementation for Perspective Plane Program Induction from a Single Image (P3I).
PyTorch implementation for the Neuro-Symbolic Concept Learner (NS-CL).
Usable implementation of Emerging Symbol Binding Network (ESBN), in Pytorch
Vertex-Enriched Graph Neural Network (VEGNN)
An attempt to merge ESBN with Transformers, to endow Transformers with the ability to emergently bind symbols
Neuro-Symbolic Visual Question Answering on Sort-of-CLEVR using PyTorch
Master's thesis : Knowledge Inference and Knowledge Completion Methods using Neuro-Symbolic Inductive Rules
Implementation of a straight-through gradient wrapper to allow for discrete latent representations. Provides binary discretizer which maps hidden representations to {0, 1} and a learnable multi-value discretizer, which maps hidden activations to their closest value in a set of given size.
Python library that enables using prolog syntax and logic programming in python
A novel approach to learning concept embeddings and approximate reasoning in ALC knowledge bases with deep neural networks
Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.
BotGNN: Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
The official repository for the PSYCHIC model
Code for "ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition" (LREC-COLING 2024)
Holographic Reduced Representations
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.
AIKA is a new type of artificial neural network designed to more closely mimic the behavior of a biological brain and to bridge the gap to classical AI. A key design decision in the Aika network is to conceptually separate the activations from their neurons, meaning that there are two separate graphs. One graph consisting of neurons and synapses…
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