Dynamical Wasserstein Barycenters for Time Series Modeling

Related tags

Deep LearningDynamicalWassBarycenters_Gaussian
Overview

Dynamical Wasserstein Barycenters for Time Series Modeling

This is the code related for the Dynamical Wasserstein Barycenter model published in Neurips 2021.

To run the code and replicate the results reported in our paper,

# usage: DynamicalWassersteinBarycenters.py dataSet dataFile debugFolder interpModel [--ParamTest PARAMTEST] [--lambda LAM] [--s S]

# Sample run on MSR data                                         
>> python DynamicalWassersteinBarycenters.py MSR_Batch ../Data/MSR_Data/subj090_1.mat ../debug/MSR/subj001_1.mat Wass 

# Sample run for parameter test
>> python DynamicalWassersteinBarycenters.py MSR_Batch ../Data/MSR_Data/subj090_1.mat ../debug/ParamTest/subj001_1.mat Wass --ParamTest 1 --lambda 100 --s 1.0

The interpMethod is either Wass` for the Wasserstein barycentric model or GMM`` for the linear interpolation model.

Simulated Data

The simulated data and experiment included in this supplement can be replicated using using the following commands.

# Generate 2 and 3 state simulated data                                         
>> python GenerateOptimizationExperimentData.py
>> python GenerateOptimizationExperimentData_3K.py

# usage: OptimizationExperiment.py FileIn Mode File
# Sample run for optimization experiment
>> python OptimizationExperiment.py ../data/SimulatedOptimizationData_2K/dim_5_5.mat/ WB ../debug/SimulatedData/dim_5_5_out.mat

The Mode is either WB for Wasserstein-Bures geometry and Euc for Euclidean geometry using Cholesky decomposition parameterization.

Requirements

_libgcc_mutex=0.1=conda_forge
_openmp_mutex=4.5=1_llvm
_pytorch_select=0.2=gpu_0
blas=2.17=openblas
ca-certificates=2020.12.5=ha878542_0
certifi=2020.12.5=py38h578d9bd_1
cffi=1.14.4=py38h261ae71_0
cudatoolkit=8.0=3
cudnn=7.1.3=cuda8.0_0
cycler=0.10.0=py_2
freetype=2.10.4=h7ca028e_0
future=0.18.2=py38h578d9bd_3
immutables=0.15=py38h497a2fe_0
intel-openmp=2020.2=254
joblib=1.0.0=pyhd8ed1ab_0
jpeg=9d=h36c2ea0_0
kiwisolver=1.3.1=py38h82cb98a_0
lcms2=2.11=hcbb858e_1
ld_impl_linux-64=2.33.1=h53a641e_7
libblas=3.8.0=17_openblas
libcblas=3.8.0=17_openblas
libedit=3.1.20191231=h14c3975_1
libffi=3.3=he6710b0_2
libgcc-ng=9.3.0=h5dbcf3e_17
libgfortran-ng=7.3.0=hdf63c60_0
libgomp=9.3.0=h5dbcf3e_17
liblapack=3.8.0=17_openblas
liblapacke=3.8.0=17_openblas
libopenblas=0.3.10=pthreads_hb3c22a3_4
libpng=1.6.37=h21135ba_2
libstdcxx-ng=9.3.0=h6de172a_18
libtiff=4.1.0=h4f3a223_6
libwebp-base=1.1.0=h36c2ea0_3
llvm-openmp=11.0.0=hfc4b9b4_1
lz4-c=1.9.2=he1b5a44_3
matplotlib-base=3.3.3=py38h5c7f4ab_0
mkl=2020.4=h726a3e6_304
mkl-service=2.3.0=py38he904b0f_0
mkl_fft=1.3.0=py38h5c078b8_1
mkl_random=1.2.0=py38hc5bc63f_1
ncurses=6.2=he6710b0_1
ninja=1.10.2=py38hff7bd54_0
numpy=1.19.5=py38h18fd61f_1
numpy-base=1.18.5=py38h2f8d375_0
olefile=0.46=pyh9f0ad1d_1
openssl=1.1.1k=h7f98852_0
pillow=8.1.0=py38h357d4e7_1
pip=20.3.3=py38h06a4308_0
pot=0.7.0=py38h950e882_0
pycparser=2.20=py_2
pyparsing=2.4.7=pyh9f0ad1d_0
python=3.8.5=h7579374_1
python-dateutil=2.8.1=py_0
python_abi=3.8=1_cp38
pytorch=1.7.1=cpu_py38h36eccb8_1
readline=8.0=h7b6447c_0
scikit-learn=0.24.1=py38h658cfdd_0
scipy=1.5.2=py38h8c5af15_0
setuptools=51.1.2=py38h06a4308_4
six=1.15.0=py38h06a4308_0
sqlite=3.33.0=h62c20be_0
threadpoolctl=2.1.0=pyh5ca1d4c_0
tk=8.6.10=hbc83047_0
tornado=6.1=py38h497a2fe_1
wheel=0.36.2=pyhd3eb1b0_0
xz=5.2.5=h7b6447c_0
zlib=1.2.11=h7b6447c_3
zstd=1.4.5=h6597ccf_2
Owner
GitHub Repository
Tool cek opsi checkpoint facebook!

tool apa ini? cek_opsi_facebook adalah sebuah tool yang mengecek opsi checkpoint akun facebook yang terkena checkpoint! tujuan dibuatnya tool ini? too

Muhammad Latif Harkat 2 Jul 17, 2022
CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels Accurate pressure drop estimat

Alejandro Montanez 0 Jan 21, 2022
CBKH: The Cornell Biomedical Knowledge Hub

Cornell Biomedical Knowledge Hub (CBKH) CBKG integrates data from 18 publicly available biomedical databases. The current version of CBKG contains a t

44 Dec 21, 2022
Official PyTorch implementation of "Adversarial Reciprocal Points Learning for Open Set Recognition"

Adversarial Reciprocal Points Learning for Open Set Recognition Official PyTorch implementation of "Adversarial Reciprocal Points Learning for Open Se

Guangyao Chen 78 Dec 28, 2022
Learning Neural Network Subspaces

Learning Neural Network Subspaces Welcome to the codebase for Learning Neural Network Subspaces by Mitchell Wortsman, Maxwell Horton, Carlos Guestrin,

Apple 117 Nov 17, 2022
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

Phil Wang 1.5k Jan 02, 2023
HuSpaCy: industrial-strength Hungarian natural language processing

HuSpaCy: Industrial-strength Hungarian NLP HuSpaCy is a spaCy model and a library providing industrial-strength Hungarian language processing faciliti

HuSpaCy 120 Dec 14, 2022
Toolbox of models, callbacks, and datasets for AI/ML researchers.

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch Website • Installation • Main

Pytorch Lightning 1.4k Dec 30, 2022
League of Legends Reinforcement Learning Environment (LoLRLE) multiple training scenarios using PPO.

League of Legends Reinforcement Learning Environment (LoLRLE) About This repo contains code to train an agent to play league of legends in a distribut

2 Aug 19, 2022
This code is a toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation.

ERFNet This code is a toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation. NEW!! New PyTorch

Edu 104 Jan 05, 2023
Generative code template for PixelBeasts 10k NFT project.

generator-template Generative code template for combining transparent png attributes into 10,000 unique images. Used for the PixelBeasts 10k NFT proje

Yohei Nakajima 9 Aug 24, 2022
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud This repository contains a reference implementation of our Part-Aware Data Augment

Jaeseok Choi 62 Jan 03, 2023
A collection of differentiable SVD methods and also the official implementation of the ICCV21 paper "Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?"

Differentiable SVD Introduction This repository contains: The official Pytorch implementation of ICCV21 paper Why Approximate Matrix Square Root Outpe

YueSong 32 Dec 25, 2022
Hide screen when boss is approaching.

BossSensor Hide your screen when your boss is approaching. Demo The boss stands up. He is approaching. When he is approaching, the program fetches fac

Hiroki Nakayama 6.2k Jan 07, 2023
UniFormer - official implementation of UniFormer

UniFormer This repo is the official implementation of "Uniformer: Unified Transf

SenseTime X-Lab 573 Jan 04, 2023
PyTorch implementations of the NeRF model described in "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"

PyTorch NeRF and pixelNeRF NeRF: Tiny NeRF: pixelNeRF: This repository contains minimal PyTorch implementations of the NeRF model described in "NeRF:

Michael A. Alcorn 178 Dec 20, 2022
Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".

nvdiffrec Joint optimization of topology, materials and lighting from multi-view image observations as described in the paper Extracting Triangular 3D

NVIDIA Research Projects 1.4k Jan 01, 2023
使用OpenCV部署全景驾驶感知网络YOLOP，可同时处理交通目标检测、可驾驶区域分割、车道线检测，三项视觉感知任务，包含C++和Python两种版本的程序实现。本套程序只依赖opencv库就可以运行， 从而彻底摆脱对任何深度学习框架的依赖。

YOLOP-opencv-dnn 使用OpenCV部署全景驾驶感知网络YOLOP，可同时处理交通目标检测、可驾驶区域分割、车道线检测，三项视觉感知任务，依然是包含C++和Python两种版本的程序实现 onnx文件从百度云盘下载，链接：https://pan.baidu.com/s/1A_9cldU

178 Jan 07, 2023
A FAIR dataset of TCV experimental results for validating edge/divertor turbulence models.

TCV-X21 validation for divertor turbulence simulations Quick links Intro Welcome to TCV-X21. We're glad you've found us! This repository is designed t

0 Dec 18, 2021
一个多语言支持、易使用的 OCR 项目。An easy-to-use OCR project with multilingual support.

AgentOCR 简介 AgentOCR 是一个基于 PaddleOCR 和 ONNXRuntime 项目开发的一个使用简单、调用方便的 OCR 项目 本项目目前包含 Python Package 【AgentOCR】 和 OCR 标注软件 【AgentOCRLabeling】 使用指南 Pytho

AgentMaker 98 Nov 10, 2022