Python Variational-monte-carlo Resources

ChemRxiv | [Paper] XXX DeepStruc Welcome to DeepStruc, a Deep Generative Model (DGM) that learns the relation between PDF and atomic structure and the

MABe_2022_TVAE: a Trajectory Variational Autoencoder baseline for the 2022 Multi-Agent Behavior challenge This repository contains jupyter notebooks t

ProbAI 2021 - Probabilistic Programming and Variational Inference Tutorial with Pryo Day 1 (June 14) Slides Notebook: students_PPLs_Intro Notebook: so

RAVE: Realtime Audio Variational autoEncoder Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio synthes

GP-VAE This repository provides datasets and code for preprocessing, training and testing models for the paper: Diverse Text Generation via Variationa

scAR scAR (single cell Ambient Remover) is a package for denoising multiple single cell omics data. It can be used for multiple tasks, such as, sgRNA

A variational Bayesian method for similarity learning in non-rigid image registration We provide the source code and the trained models used in the re

PyElecCL Python program to perform second checks for electron Monte Carlo radiat

mini-hmc-jax This is a simple implementation of Hamiltonian Monte Carlo in JAX t

Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical V

Split-VAE Split Variational AutoEncoder Introduction This repository contains and implemementation of a Split Variational AutoEncoder (SVAE). In a SVA

PolytopeSampler PolytopeSampler is a Matlab implementation of constrained Riemannian Hamiltonian Monte Carlo for sampling from high dimensional disrib

Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently.

This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch. The code was prepared to the final version of the accepted manuscript in AISTATS and is provided as-is.

Probabilistic U-Net + **Update** + An improved Model (the Hierarchical Probabilistic U-Net) + LIDC crops is now available. See below. Re-implementatio

Demo-of-MCMC These files are based on the class materials of AEROSP 567 taught by Prof. Alex Gorodetsky at University of Michigan. Author: Hung-Hsiang

QVC Optimizer Review Code for the paper "An Empirical Review of Optimization Techniques for Quantum Variational Circuits". Each of the python files ca

NTIRE 2022 - Image Inpainting Challenge Important dates 2022.02.01: Release of train data (input and output images) and validation data (only input) 2

Variational DiffWave Training DiffWave using variational method from Variational Diffusion Models. Quick Start python train_distributed.py discrete_10

smc.covid smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectiou

ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference, with the design goal of unifying the automation (of Monte Carlo simulations), user-friendliness (of the library), accessibility (from multiple programming environments), high-performance (at runtime), and scalability (across many parallel processors).

VarDACAE This module is used to create Convolutional AutoEncoders for Variational Data Assimilation. A user can define, create and train an AE for Dat

Official PyTorch implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 Spotlight Paper) Zhisheng

VIMuRe Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data. If you use this code please cite this article (preprint). De

LA-MCTS The code is based of paper Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. Component LA-MCTS has thr

3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces Installation After cloning the repo open

CoulombGas This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX,

Linear Variational State Space Filters To set up the environment, use the provided scripts in the docker/ folder to build and run the codebase inside

Brain-Inspired Replay A PyTorch implementation of the continual learning experiments with deep neural networks described in the following paper: Brain

pypmc pypmc is a python package focusing on adaptive importance sampling. It can be used for integration and sampling from a user-defined target densi

Task-Aware Variational Adversarial Active Learning - Official Pytorch implementation of the CVPR 2021 paper Kwanyoung Kim, Dongwon Park, Kwang In Kim,

PySDM PySDM is a package for simulating the dynamics of population of particles. It is intended to serve as a building block for simulation systems mo

Bayesian Neural Networks Pytorch implementations for the following approximate inference methods: Bayes by Backprop Bayes by Backprop + Local Reparame

Spatio-Temporal Variational GPs This repository is the official implementation of the methods in the publication: O. Hamelijnck, W.J. Wilkinson, N.A.

Documentation pythae This library implements some of the most common (Variational) Autoencoder models. In particular it provides the possibility to pe

Smoothed Mutual Information ``Lower Bound'' Estimator PyTorch implementation for the ICLR 2020 paper Understanding the Limitations of Variational Mutu

CoulombGas This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX,

NetKet NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and

vae-textures This is an experiment with using variational autoencoders (VAEs) to perform mesh parameterization. This was also my first project using J

Python script to create a TOPAS-readable simulation file descriring a Multi-Leaf-Collimator. Builds the MLC using the data from a 3D .stl file.

topas-create-graphs A script to automatically plot the results of a topas simulation Works for percentage depth dose (pdd) and dose profiles (dp). Dep

topas-merge-simulations Python script to combine the statistical results of a TOPAS simulation that was split up into multiple batches At the top of t

MT-VAE for Multimodal Human Motion Synthesis This is the code for ECCV 2018 paper MT-VAE: Learning Motion Transformations to Generate Multimodal Human

dblmahmc Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo" Requirements: https://github.com

PyTorch implementation of normalizing flow models

Monte Carlo python simulation Install linux dependencies sudo apt update sudo apt install build-essential \ software-properties-commo

ImageCompressionSimulation An Image compression simulator that uses Source Extractor and Monte Carlo methods to examine the post compressive effects o

Visual Adversarial Imitation Learning using Variational Models (VMAIL) This is the official implementation of the NeurIPS 2021 paper. Project website

Junction Tree Variational Autoencoder for Molecular Graph Generation Official implementation of our Junction Tree Variational Autoencoder https://arxi

This folder contains the code for 'Scalable Variational Approaches for Bayesian Causal Discovery'. Installation To install, use conda with conda env c

Disimpy Disimpy is a Python package for generating simulated diffusion-weighted MR signals that can be useful in the development and validation of dat

Custom Python code for calculating the Probability of Profit (POP) for options trading strategies using Monte Carlo Simulations.

l2hmc-qcd 📊 Slides Recent talk on Training Topological Samplers for Lattice Gauge Theory from the Machine Learning for High Energy Physics, on and of

Pytorch implementation of VQVAE. This paper combines 2 tricks: Vector Quantization (check out this amazing blog for better understanding.) Straight-Th

jax-variational-diffwave Jax/Flax implementation of Variational-DiffWave. (Zhifeng Kong et al., 2020, Diederik P. Kingma et al., 2021.) DiffWave with

Annealed Flow Transport Monte Carlo Open source implementation accompanying ICML 2021 paper by Michael Arbel*, Alexander G. D. G. Matthews* and Arnaud

Fast mesh denoising with data driven normal filtering using deep variational autoencoders This is an implementation for the paper entitled "Fast mesh

Variational Gibbs inference (VGI) This repository contains the research code for Simkus, V., Rhodes, B., Gutmann, M. U., 2021. Variational Gibbs infer

Contrastively Disentangled Sequential Variational Audoencoder (C-DSVAE) Overview This is the implementation for our C-DSVAE, a novel self-supervised d

HSPICE can not perform Monte Carlo (MC) simulations while considering aging effects. I developed a python wrapper that automatically performs MC and aging simulations using HPSICE to save engineering hours.

alpha-GAN Unofficial pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks." arXi

Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models, under review at ICLR 2017 requirements: T

cppn-gan-vae tensorflow Train Compositional Pattern Producing Network as a Generative Model, using Generative Adversarial Networks and Variational Aut

Ladder Variational Autoencoders (LVAE) PyTorch implementation of Ladder Variational Autoencoders (LVAE) [1]: where the variational distributions q at

tensorflow-generative-model-collections Tensorflow implementation of various GANs and VAEs. Related Repositories Pytorch version Pytorch version of th

Open-KG-canonicalization The software associated with a paper accepted at EMNLP 2021 titled "Open Knowledge Graphs Canonicalization using Variational

Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

Alpha Zero General (any game, any framework!) A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play

freud Overview The freud Python library provides a simple, flexible, powerful set of tools for analyzing trajectories obtained from molecular dynamics

Conditional Variational Capsule Network for Open Set Recognition This repository hosts the official code related to "Conditional Variational Capsule N

Dual Parameterization of Sparse Variational Gaussian Processes Documentation | Notebooks | API reference Introduction This repository is the official

Official repository of the paper (UAI 2021) "A Variational Approximation for Analyzing the Dynamics of Panel Data", Mixed Effect Neural ODE. Panel dat

Discovering Non-monotonic Autoregressive Orderings with Variational Inference Description This package contains the source code implementation of the

VACA Code repository for the paper "VACA: Designing Variational Graph Autoencoders for Interventional and Counterfactual Queries (arXiv)". The impleme

Conditioning Sparse Variational Gaussian Processes for Online Decision-making This repository contains a PyTorch and GPyTorch implementation of the pa

Variational Filtering To run phi learning on linear gaussian (Fig1a) python linear_gaussian_phi_learning.py To run phi and theta learning on linear g

PyTorch Autoencoders Implementing a Variational Autoencoder (VAE) Series in Pytorch. Inspired by this repository Model List check model paper conferen

Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders Getting Started Install requirements with Anaconda: conda env c

DU-VAE This is the pytorch implementation of the paper "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness" Acknowledgement

vmgp This is the repository of Vivek Myers and Nikhil Sardana for our CS 330 final project, Bayesian Meta-Learning Through Variational Gaussian Proces

VAE animeface Variational autoencoder for anime face reconstruction Introduction This repository is an exploratory example to train a variational auto

Focal Frequency Loss - Official PyTorch Implementation This repository provides the official PyTorch implementation for the following paper: Focal Fre

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

ST-VAE Multiple style transfer via variational autoencoder By Zhi-Song Liu, Vicky Kalogeiton and Marie-Paule Cani This repo only provides simple testi

unfoldedVBA Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution This repository contains the Pytorch implementation of the unrolled

Variational Graph Auto-encoder in Pytorch This repository implements variational graph auto-encoder by Thomas Kipf. For details of the model, refer to

Elicited Helper tools to construct probability distributions built from expert elicited data for use in monte carlo simulations. Credit to Brett Hoove

Documentation Pyraug This library provides a way to perform Data Augmentation using Variational Autoencoders in a reliable way even in challenging con

Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders

[TensorFlow 2] Variational Inference with Normalizing Flows TensorFlow implementation of "Variational Inference with Normalizing Flows" [1] Concept Co

Pytorch Recurrent Variational Autoencoder Model: This is the implementation of Samuel Bowman's Generating Sentences from a Continuous Space with Kim's

Learning Opinion Summarizers by Selecting Informative Reviews This repository contains the codebase and the dataset for the corresponding EMNLP 2021

mc3g Monte Carlo simulation of 3G rules This project contains the Python code to do simulations of events according to the 3G rule (in German: "Geimpf

DKPNet ICCV 2021 Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting Baseline of DKPNet is availa

lgamma Implementations of polygamma, lgamma, and beta functions for PyTorch. It's very hacky, but that's usually ok for research use. To build, run: .

transformers-into-vaes Code for Finetuning Pretrained Transformers into Variational Autoencoders (our submission to NLP Insights Workshop 2021). Gathe

MuseMorphose This repository contains the official implementation of the following paper: Shih-Lun Wu, Yi-Hsuan Yang MuseMorphose: Full-Song and Fine-

Noisy Natural Gradient as Variational Inference PyTorch implementation of Noisy Natural Gradient as Variational Inference. Requirements Python 3 Pytor

VariationalRecurrentNeuralNetwork Pytorch implementation of the Variational RNN (VRNN), from A Recurrent Latent Variable Model for Sequential Data. Th

VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.