Neural Logic Inductive Learning

Related tags

Deep Learning NLIL
Overview

Neural Logic Inductive Learning

This is the implementation of the Neural Logic Inductive Learning model (NLIL) proposed in the ICLR 2020 paper: Learn to Explain Efficiently via Neural Logic Inductive Learning. The Transformer implementation is based on this repo.

Requirements

  • python 3.6+
  • pytorch 1.1.0+
  • numpy
  • tqdm

Knowledge completion on WN18 and FB15K

You can run knowledge completion task on WN18 and FB15K with provided scripts

bash run_wn.sh
bash run_fb.sh

Object classification on Visual Genome

First, download the scene-graph dataset from the official site (click "Download Scene Graphs")

https://cs.stanford.edu/people/dorarad/gqa/download.html

Extract the files, and run the following script to generate the dataset

bash preprocess.sh path/to/the/sgraph/folder

Now you can run object classification with

bash run_gqa.sh

Reference

@inproceedings{
    yang2020learn,
    title={Learn to Explain Efficiently via Neural Logic Inductive Learning},
    author={Yuan Yang and Le Song},
    booktitle={International Conference on Learning Representations},
    year={2020},
    url={https://openreview.net/forum?id=SJlh8CEYDB}
}
You might also like...
An implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks in PyTorch.

Neural Attention Distillation This is an implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep

Yige-Li 84 Jan 4, 2023
Differentiable Neural Computers, Sparse Access Memory and Sparse Differentiable Neural Computers, for Pytorch
Differentiable Neural Computers, Sparse Access Memory and Sparse Differentiable Neural Computers, for Pytorch

Differentiable Neural Computers and family, for Pytorch Includes: Differentiable Neural Computers (DNC) Sparse Access Memory (SAM) Sparse Differentiab

ixaxaar 302 Dec 14, 2022
Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom
Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom

Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom Sample on-line plotting while training(avg loss)/testing(writ

Jingwei Zhang 269 Nov 15, 2022
Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

youceF 1 Nov 12, 2021
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

What is DeepHyper? DeepHyper is a software package that uses learning, optimization, and parallel computing to automate the design and development of

DeepHyper Team 214 Jan 8, 2023
This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural tree born form a large search space
This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural tree born form a large search space

SeBoW: Self-Born Wiring for neural trees(PaddlePaddle version) This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural

HollyLee 13 Dec 8, 2022
Neural-fractal - Create Fractals Using Complex-Valued Neural Networks!

Neural Fractal Create Fractals Using Complex-Valued Neural Networks! Home Page Features Define Dynamical Systems Using Complex-Valued Neural Networks

Amirabbas Asadi 10 Dec 17, 2022
Neural-net-from-scratch - A simple Neural Network from scratch in Python using the Pymathrix library

A Simple Neural Network from scratch A Simple Neural Network from scratch in Pyt

Youssef Chafiqui 2 Jan 7, 2022
This is a model made out of Neural Network specifically a Convolutional Neural Network model
This is a model made out of Neural Network specifically a Convolutional Neural Network model

This is a model made out of Neural Network specifically a Convolutional Neural Network model. This was done with a pre-built dataset from the tensorflow and keras packages. There are other alternative libraries that can be used for this purpose, one of which is the PyTorch library.

null 9 Oct 18, 2022
Comments
  • inference

    inference

    Can you please provide a simple example of how to work with the trained model? As I understand it, once trained on a KB, it should generate FOL rules? Does it save them somewhere? Or we can get them interactively from the model?

    opened by PolKul 0
  • Issue running evensucc10 dataset

    Issue running evensucc10 dataset

    By simply changing the learning folder to the evensucc10 dataset through:

    --data_root ../data/evensucc10

    yields an error:

      File "<...>/model/SubLayers.py", line 45, in forward
    q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k)  # (n*b) x lq x dk
RuntimeError: cannot reshape tensor of 0 elements into shape [-1, 0, 32] because the unspecified dimension size -1 can be any value and is ambiguous

    I believe it is caused by

    mldel/Models.py: line 200~204: # TODO: debug ...

    Can you help me take a look into this issue?

    opened by moqingyan 2
  • Questions about model implementation

    Questions about model implementation

    Hi, I am very interested in your work.But I have some questions

    The Operator search of Section 4 in the Paper is as follows

    the EncoderLayer(nn.Module) take Q and V as the input . image

    However，the EncoderLayer(nn.Module) in your code dose not use Q as the input. The code just take the V as the input .

    image

    In addition, you use Q instead of q as input in DecoderLayer(nn.Module).

    Is there a mistake in my understanding? Can you answer this doubt for me?

    opened by lfxx123 1
Owner
null
GitHub
Official implementation of the ICCV 2021 paper "Joint Inductive and Transductive Learning for Video Object Segmentation"

JOINT This is the official implementation of Joint Inductive and Transductive learning for Video Object Segmentation, to appear in ICCV 2021. @inproce

Yunyao 35 Oct 16, 2022
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

This repository holds the implementation for paper Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach Download our preproc

Qitian Wu 42 Dec 27, 2022
"Inductive Entity Representations from Text via Link Prediction" @ The Web Conference 2021

Inductive entity representations from text via link prediction This repository contains the code used for the experiments in the paper "Inductive enti

Daniel Daza 45 Jan 9, 2023
Study of human inductive biases in CNNs and Transformers.

Are Convolutional Neural Networks or Transformers more like human vision? This repository contains the code and fine-tuned models of popular Convoluti

Shikhar Tuli 39 Dec 8, 2022
DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predicate.

DeepProbLog DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predic

KU Leuven Machine Learning Research Group 94 Dec 18, 2022
A PyTorch Implementation of "Neural Arithmetic Logic Units"

Neural Arithmetic Logic Units [WIP] This is a PyTorch implementation of Neural Arithmetic Logic Units by Andrew Trask, Felix Hill, Scott Reed, Jack Ra

Kevin Zakka 181 Nov 18, 2022
PyTorch implementation for the Neuro-Symbolic Sudoku Solver leveraging the power of Neural Logic Machines (NLM)

Neuro-Symbolic Sudoku Solver PyTorch implementation for the Neuro-Symbolic Sudoku Solver leveraging the power of Neural Logic Machines (NLM). Please n

Ashutosh Hathidara 60 Dec 10, 2022
This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

The Neural Process Family This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CN

DeepMind 892 Dec 28, 2022
Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20

Despoina Paschalidou 161 Dec 20, 2022
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

Intel Labs 210 Jan 4, 2023