DeepStochlog Package For Python

Related tags

Deep Learningdeepstochlog
Overview

DeepStochLog

Installation

Installing SWI Prolog

DeepStochLog requires SWI Prolog to run. Run the following commands to install:

sudo apt-add-repository ppa:swi-prolog/stable
sudo apt-get update
sudo apt-get install swi-prolog

Installing DeepStochLog package

To install DeepStochLog itself, run the following command:

pip install deepstochlog

Running the examples

Local dependencies

To see DeepStochLog in action, please first install SWI Prolog (as explained about), as well as the requirements listed in requirements.txt

pip install -r requirements.txt

Datasets

The datasets used in the tasks used to evaluate DeepStochLog can be found in our initial release.

Addition example

To see DeepStochLog in action, navigate to examples/addition and run addition.py.

The neural definite clause grammar specification is provided in addition.pl. The addition(N) predicate specifies/recognises that two handwritten digits N1 and N2 sum to N. The neural probability nn(number, [X], Y, digit) makes the neural network with name number (a MNIST classifier) label input image X with the digit Y.

Credits & Paper citation

If use this work in an academic context, please consider citing the following paper:

The paper is also accepted to AAAI22. Please cite that version of the paper when the proceedings are out.

@article{winters2021deepstochlog,
  title={Deepstochlog: Neural stochastic logic programming},
  author={Winters, Thomas and Marra, Giuseppe and Manhaeve, Robin and De Raedt, Luc},
  journal={arXiv preprint arXiv:2106.12574},
  year={2021}
}
You might also like...
This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters.
This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters.

openmc-plasma-source This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters. The OpenMC sources a

Fusion Energy 10 Oct 18, 2022
A Python Package for Portfolio Optimization using the Critical Line Algorithm

PyCLA A Python Package for Portfolio Optimization using the Critical Line Algorithm Getting started To use PyCLA, clone the repo and install the requi

null 19 Oct 11, 2022
HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events globally on daily to subseasonal timescales.

HeatNet HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events glob

Google Research 6 Jul 7, 2022
NLMpy - A Python package to create neutral landscape models
NLMpy - A Python package to create neutral landscape models

NLMpy is a Python package for the creation of neutral landscape models that are widely used by landscape ecologists to model ecological patterns

Manaaki Whenua – Landcare Research 1 Oct 8, 2022
QuakeLabeler is a Python package to create and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing.
QuakeLabeler is a Python package to create and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing.

QuakeLabeler Quake Labeler was born from the need for seismologists and developers who are not AI specialists to easily, quickly, and independently bu

Hao Mai 15 Nov 4, 2022
Python package provinding tools for artistic interactive applications using AI

Documentation redrawing Python package provinding tools for artistic interactive applications using AI Created by ReDrawing Campinas team for the Open

ReDrawing Campinas 1 Sep 30, 2021
The hippynn python package - a modular library for atomistic machine learning with pytorch.

The hippynn python package - a modular library for atomistic machine learning with pytorch. We aim to provide a powerful library for the training of a

Los Alamos National Laboratory 37 Dec 29, 2022
Python package for covariance matrices manipulation and Biosignal classification with application in Brain Computer interface

pyRiemann pyRiemann is a python package for covariance matrices manipulation and classification through Riemannian geometry. The primary target is cla

null 447 Jan 5, 2023
A Python package to process & model ChEMBL data.

insilico: A Python package to process & model ChEMBL data. ChEMBL is a manually curated chemical database of bioactive molecules with drug-like proper

Steven Newton 0 Dec 9, 2021
Releases(0.0.1)
Owner
KU Leuven Machine Learning Research Group
KU Leuven Machine Learning Research Group
GitHub Repository
Official code for: A Probabilistic Hard Attention Model For Sequentially Observed Scenes

"A Probabilistic Hard Attention Model For Sequentially Observed Scenes" Authors: Samrudhdhi Rangrej, James Clark Accepted to: BMVC'21 A recurrent atte

5 Nov 19, 2022
Video Representation Learning by Recognizing Temporal Transformations. In ECCV, 2020.

Video Representation Learning by Recognizing Temporal Transformations [Project Page] Simon Jenni, Givi Meishvili, and Paolo Favaro. In ECCV, 2020. Thi

Simon Jenni 46 Nov 14, 2022
Continuous Conditional Random Field Convolution for Point Cloud Segmentation

CRFConv This repository is the implementation of "Continuous Conditional Random Field Convolution for Point Cloud Segmentation" 1. Setup 1) Building c

Fei Yang 8 Dec 08, 2022
Privacy-Preserving Portrait Matting [ACM MM-21]

Privacy-Preserving Portrait Matting [ACM MM-21] This is the official repository of the paper Privacy-Preserving Portrait Matting. Jizhizi Li∗, Sihan M

Jizhizi_Li 212 Dec 27, 2022
Contrastive Learning for Metagenomic Binning

CLMB A simple framework for CLMB - a novel deep Contrastive Learningfor Metagenomic Binning Created by Pengfei Zhang, senior of Department of Computer

1 Sep 14, 2022
Some experiments with tennis player aging curves using Hilbert space GPs in PyMC. Only experimental for now.

NOTE: This is still being developed! Setup notes This document uses Jeff Sackmann's tennis data. You can obtain it as follows: git clone https://githu

Martin Ingram 1 Jan 20, 2022
Dynamic Token Normalization Improves Vision Transformers

Dynamic Token Normalization Improves Vision Transformers This is the PyTorch implementation of the paper Dynamic Token Normalization Improves Vision T

Wenqi Shao 20 Oct 09, 2022
Serverless proxy for Spark cluster

Hydrosphere Mist Hydrosphere Mist is a serverless proxy for Spark cluster. Mist provides a new functional programming framework and deployment model f

hydrosphere.io 317 Dec 01, 2022
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

DingDing 143 Jan 01, 2023
A best practice for tensorflow project template architecture.

A best practice for tensorflow project template architecture.

Mahmoud Gamal Salem 3.6k Dec 22, 2022
Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at [email protected]

TableParser Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at DS3 Lab 11 Dec 13, 2022

Code for MarioNette: Self-Supervised Sprite Learning, in NeurIPS 2021

MarioNette | Webpage | Paper | Video MarioNette: Self-Supervised Sprite Learning Dmitriy Smirnov, Michaël Gharbi, Matthew Fisher, Vitor Guizilini, Ale

Dima Smirnov 28 Nov 18, 2022
Object-aware Contrastive Learning for Debiased Scene Representation

Object-aware Contrastive Learning Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo

43 Dec 14, 2022
Implementation of ICCV21 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers

Implementation of ICCV 2021 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers arxiv This repository is based on detr Recently, DETR

twang 113 Dec 27, 2022
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

AI Wizards for Software Management (AWSM) Research Group 14 Nov 13, 2022
Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"

Hold me tight! Influence of discriminative features on deep network boundaries This is the source code to reproduce the experiments of the NeurIPS 202

EPFL LTS4 19 Dec 10, 2021
an implementation of 3D Ken Burns Effect from a Single Image using PyTorch

3d-ken-burns This is a reference implementation of 3D Ken Burns Effect from a Single Image [1] using PyTorch. Given a single input image, it animates

Simon Niklaus 1.4k Dec 28, 2022
Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Ng Kam Woh 71 Dec 22, 2022
Repository containing the PhD Thesis "Formal Verification of Deep Reinforcement Learning Agents"

Getting Started This repository contains the code used for the following publications: Probabilistic Guarantees for Safe Deep Reinforcement Learning (

Edoardo Bacci 5 Aug 31, 2022
Code for Understanding Pooling in Graph Neural Networks

Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install

Daniele Grattarola 37 Dec 13, 2022