A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

Related tags

Deep Learningpytorchrestricted-boltzmann-machineprobabilistic-graphical-modelsdeep-belief-network
Overview

README of "PyTorch-ProbGraph"

What is PyTorch-ProbGraph?

PyTorch-ProbGraph is a library based on amazing PyTorch (https://pytorch.org) to easily use and adapt directed and undirected Hierarchical Probabilistic Graphical Models. These include Restricted Boltzmann Machines, Deep Belief Networks, Deep Boltzmann Machines and Helmholtz Machines (Sigmoid Belief Networks).

Models can be set up in a modular fashion, using UnitLayers, layers of Random Units and Interactions between these UnitLayers. Currently, only Gaussian, Categorical and Bernoulli units are available, but an extension can be made to allow all kinds of distributions from the Exponential family. (see https://en.wikipedia.org/wiki/Exponential_family)

The Interactions are usually only linear for undirected models, but can be built from arbitrary PyTorch torch.nn.Modules (using forward and the backward gradient).

There is a pre-implemented fully-connected InteractionLinear, one for using existing torch.nn.Modules and some custom Interactions / Mappings to enable Probabilistic Max-Pooling. Interactions can also be connected without intermediate Random UnitLayers with InteractionSequential.

This library was built by Korbinian Poeppel and Hendrik Elvers during a Practical Course "Beyond Deep Learning - Uncertainty Aware Models" at TU Munich. Disclaimer: It is built as an extension to PyTorch and not directly affiliated.

Documentation

A more detailed documentation is included, using the Sphinx framework. Go inside directory 'docs' and run 'make html' (having Sphinx installed). The documentation can then be found inside the _build sub-directory.

Examples

There are some example models, as well as an evaluation script using the EMNIST dataset in the examples folder.

License

This library is distributed in a BSD 3-clause license.

Setup

The library is accessible via the PyPi repository and can be install by: pip install pytorch_probgraph

References

Ian Goodfellow and Yoshua Bengio and Aaron Courville, http://www.deeplearningbook.org

Jörg Bornschein, Yoshua Bengio Reweighted Wake-Sleep https://arxiv.org/abs/1406.2751

Geoffrey Hinton, A Practical Guide to Training Restricted Boltzmann Machines https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf

Ruslan Salakhutdinov, Learning Deep Generative Models https://tspace.library.utoronto.ca/handle/1807/19226

Honglak Lee et al., Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, ICML09

G.Hinton, S. Osindero A fast learning algorithm for deep belief nets

You might also like...
Scikit-learn compatible estimation of general graphical models
Scikit-learn compatible estimation of general graphical models

skggm : Gaussian graphical models using the scikit-learn API In the last decade, learning networks that encode conditional independence relationships

null 213 Jan 2, 2023
Simple PyTorch hierarchical models.
Simple PyTorch hierarchical models.

A python package adding basic hierarchal networks in pytorch for classification tasks. It implements a simple hierarchal network structure based on feed-backward outputs.

Rajiv Sarvepalli 5 Mar 6, 2022
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Attack-Probabilistic-Models This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. This repository contai

SRI Lab, ETH Zurich 25 Sep 14, 2022
Denoising Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models This repo contains code for DDPM training. Based on Denoising Diffusion Probabilistic Models, Improved Denois

Alexander Markov 7 Dec 15, 2022
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral)
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral)

ILVR + ADM This is the implementation of ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral). This repository is h

Jooyoung Choi 225 Dec 28, 2022
Topic Modelling for Humans

gensim – Topic Modelling in Python Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Targ

RARE Technologies 13.8k Jan 3, 2023
Civsim is a basic civilisation simulation and modelling system built in Python 3.8.
Civsim is a basic civilisation simulation and modelling system built in Python 3.8.

Civsim Introduction Civsim is a basic civilisation simulation and modelling system built in Python 3.8. It requires the following packages: perlin_noi

null 17 Aug 8, 2022
Dataloader tools for language modelling

Installation: pip install lm_dataloader Design Philosophy A library to unify lm dataloading at large scale Simple interface, any tokenizer can be inte

null 5 Mar 25, 2022
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations Code repo for paper Trans-Encoder: Unsupervised sentence-pa

Amazon 101 Dec 29, 2022
Releases(v0.1-beta)
Owner
Korbinian Pöppel
Korbinian Pöppel
GitHub Repository
[KDD 2021, Research Track] DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

DiffMG This repository contains the code for our KDD 2021 Research Track paper: DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neura

AutoML Research 24 Nov 29, 2022
Python utility to generate filesystem content for Obsidian.

Security Vault Generator Quickly parse, format, and output common frameworks/content for Obsidian.md. There is a strong focus on MITRE ATT&CK because

Justin Angel 73 Dec 02, 2022
Semi-Supervised Signed Clustering Graph Neural Network (and Implementation of Some Spectral Methods)

SSSNET SSSNET: Semi-Supervised Signed Network Clustering For details, please read our paper. Environment Setup Overview The project has been tested on

Yixuan He 9 Nov 24, 2022
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR)

Ilya Kostrikov 3k Dec 31, 2022
PyTorch implementation of Barlow Twins.

Barlow Twins: Self-Supervised Learning via Redundancy Reduction PyTorch implementation of Barlow Twins. @article{zbontar2021barlow, title={Barlow Tw

Facebook Research 839 Dec 29, 2022
YOLOX-Paddle - A reproduction of YOLOX by PaddlePaddle

YOLOX-Paddle A reproduction of YOLOX by PaddlePaddle 数据集准备 下载COCO数据集，准备为如下路径 /ho

QuanHao Guo 6 Dec 18, 2022
🕺Full body detection and tracking

Pose-Detection 🤔 Overview Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign

Abbas Ataei 20 Nov 21, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

Aladdin Persson 4.7k Jan 08, 2023
an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 985 Jan 08, 2023
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target i

NanYoMy 13 Oct 09, 2022
High performance Cross-platform Inference-engine, you could run Anakin on x86-cpu,arm, nv-gpu, amd-gpu,bitmain and cambricon devices.

Anakin2.0 Welcome to the Anakin GitHub. Anakin is a cross-platform, high-performance inference engine, which is originally developed by Baidu engineer

514 Dec 28, 2022
Source code for 2021 ICCV paper "In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces"

In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces This is the PyTorch implementation for 2021 ICCV paper "In-the-Wild Single C

27 Dec 06, 2022
The official implementation of Equalization Loss v1 & v2 (CVPR 2020, 2021) based on MMDetection.

The Equalization Losses for Long-tailed Object Detection and Instance Segmentation This repo is official implementation CVPR 2021 paper: Equalization

Jingru Tan 129 Dec 16, 2022
Domain Adaptation with Invariant RepresentationLearning: What Transformations to Learn?

Domain Adaptation with Invariant RepresentationLearning: What Transformations to Learn? Repository Structure: DSAN |└───amazon |    └── dataset (Amazo

DMIRLAB 17 Jan 04, 2023
Plugin for Gaffer providing direct acess to asset from PolyHaven.com. Only HDRIs at the moment, Cycles and Arnold supported

GafferHaven Plugin for Gaffer providing direct acess to asset from PolyHaven.com. Only HDRIs are supported at the moment, in Cycles and Arnold lights.

Jakub Vondra 6 Jan 26, 2022
Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer"

StyleAttack Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer" Prepare Pois

THUNLP 19 Nov 20, 2022
A new test set for ImageNet

ImageNetV2 The ImageNetV2 dataset contains new test data for the ImageNet benchmark. This repository provides associated code for assembling and worki

186 Dec 18, 2022
RIM: Reliable Influence-based Active Learning on Graphs.

RIM: Reliable Influence-based Active Learning on Graphs. This repository is the official implementation of RIM. Requirements To install requirements:

Wentao Zhang 4 Aug 29, 2022
LSTMs (Long Short Term Memory) RNN for prediction of price trends

Price Prediction with Recurrent Neural Networks LSTMs BTC-USD price prediction with deep learning algorithm. Artificial Neural Networks specifically L

5 Nov 12, 2021