Topic: neuro-symbolic-learning Goto Github
Some thing interesting about neuro-symbolic-learning
Some thing interesting about neuro-symbolic-learning
neuro-symbolic-learning,Pytorch implementation for Perspective Plane Program Induction from a Single Image (P3I).
User: 42x00
Home Page: http://p3i.csail.mit.edu/
neuro-symbolic-learning,An efficient Python toolkit for Abductive Learning (ABL), a novel paradigm that integrates machine learning and logical reasoning in a unified framework.
User: abductivelearning
Home Page: https://ablkit.readthedocs.io/
neuro-symbolic-learning,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 representing the knowledge the network has already acquired and another graph consisting of activations and links describing the information the network was able to infer about a concrete input data set. There is a one-to-many relation between the neurons and the activations. For example, there might be a neuron representing a word or a specific meaning of a word, but there might be several activations of this neuron, each representing an occurrence of this word within the input data set. A consequence of this decision is that we have to give up on the idea of a fixed layered topology for the network, since the sequence in which the activations are fired depends on the input data set. Within the activation network, each activation is grounded within the input data set, even if there are several activations in between. This means links between activations serve two purposes. On the one hand, they are used to sum up the synapse weights and, on the other hand they propagate the identity to higher level activations.
User: aika-algorithm
Home Page: https://aika.network
neuro-symbolic-learning,A collection of papers of neural-symbolic AI (mainly focus on NLP applications)
User: ccclyu
neuro-symbolic-learning,Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.
User: crunchiness
Home Page: https://ingvaras.com
neuro-symbolic-learning,Tree Stack Memory Units
User: forougha
Home Page: https://arxiv.org/abs/1911.01545
neuro-symbolic-learning,Implementation for the Neural Logic Machines (NLM).
Organization: google
Home Page: https://sites.google.com/view/neural-logic-machines
neuro-symbolic-learning,The official repository for the PSYCHIC model
User: hannaabiakl
neuro-symbolic-learning,Code for "ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition" (LREC-COLING 2024)
User: hriaz17
neuro-symbolic-learning,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.
User: lgirrbach
neuro-symbolic-learning,Usable implementation of Emerging Symbol Binding Network (ESBN), in Pytorch
User: lucidrains
neuro-symbolic-learning,An attempt to merge ESBN with Transformers, to endow Transformers with the ability to emergently bind symbols
User: lucidrains
neuro-symbolic-learning,Holographic Reduced Representations
User: mahmudulalam
Home Page: https://ieeexplore.ieee.org/document/377968
neuro-symbolic-learning,A novel approach to learning concept embeddings and approximate reasoning in ALC knowledge bases with deep neural networks
User: maxadamski
neuro-symbolic-learning,RelNN is a novel first-order deep neural model for relational learning.
User: mehran-k
neuro-symbolic-learning,Python library that enables using prolog syntax and logic programming in python
User: mnoorfawi
neuro-symbolic-learning,Neuro-Symbolic Visual Question Answering on Sort-of-CLEVR using PyTorch
User: nerdimite
neuro-symbolic-learning,Master's thesis : Knowledge Inference and Knowledge Completion Methods using Neuro-Symbolic Inductive Rules
User: shinwon-chul
neuro-symbolic-learning,BotGNN: Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
User: tirtharajdash
neuro-symbolic-learning,Vertex-Enriched Graph Neural Network (VEGNN)
User: tirtharajdash
neuro-symbolic-learning,PyTorch implementation for the Neuro-Symbolic Concept Learner (NS-CL).
User: vacancy
Home Page: http://nscl.csail.mit.edu
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