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
Official implementation of "One-Shot Voice Conversion with Weight Adaptive Instance Normalization".

One-Shot Voice Conversion with Weight Adaptive Instance Normalization By Shengjie Huang, Yanyan Xu*, Dengfeng Ke*, Mingjie Chen, Thomas Hain. This rep

31 Dec 07, 2022
Pytorch implementation for "Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter".

Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter This is a pytorch-based implementation for paper Implicit Feature Alignme

wangtianwei 61 Nov 12, 2022
Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation

Kim Seonghyeon 502 Jan 03, 2023
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution

WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution This code belongs to the paper [1] available at https://arx

Fabian Altekrueger 5 Jun 02, 2022
TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations Requirements python 3.6 torch 1.9 numpy 1.19 Quick Start The experimen

DMIRLAB 4 Oct 16, 2022
A deep learning model for style-specific music generation.

DeepJ: A model for style-specific music generation https://arxiv.org/abs/1801.00887 Abstract Recent advances in deep neural networks have enabled algo

Henry Mao 704 Nov 23, 2022
Wordle Env: A Daily Word Environment for Reinforcement Learning

Wordle Env: A Daily Word Environment for Reinforcement Learning Setup Steps: git pull [email&#

2 Mar 28, 2022
Paddle-Skeleton-Based-Action-Recognition - DecoupleGCN-DropGraph, ASGCN, AGCN, STGCN

Paddle-Skeleton-Action-Recognition DecoupleGCN-DropGraph, ASGCN, AGCN, STGCN. Yo

Chenxu Peng 3 Nov 02, 2022
Alternatives to Deep Neural Networks for Function Approximations in Finance

Alternatives to Deep Neural Networks for Function Approximations in Finance Code companion repo Overview This is a repository of Python code to go wit

15 Dec 17, 2022
Official git for "CTAB-GAN: Effective Table Data Synthesizing"

CTAB-GAN This is the official git paper CTAB-GAN: Effective Table Data Synthesizing. The paper is published on Asian Conference on Machine Learning (A

30 Dec 26, 2022
Source code of our TTH paper: Targeted Trojan-Horse Attacks on Language-based Image Retrieval.

Targeted Trojan-Horse Attacks on Language-based Image Retrieval Source code of our TTH paper: Targeted Trojan-Horse Attacks on Language-based Image Re

fine 7 Aug 23, 2022
Graph WaveNet apdapted for brain connectivity analysis.

Graph WaveNet for brain network analysis This is the implementation of the Graph WaveNet model used in our manuscript: S. Wein , A. Schüller, A. M. To

4 Dec 17, 2022
PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation This is the PyTorch implemention of ICCV'21 paper SGPA: Structure

Chen Kai 24 Dec 05, 2022
SAT Project - The first project I had done at General Assembly, performed EDA, data cleaning and created data visualizations

Project 1: Standardized Test Analysis by Adam Klesc Overview This project covers: Basic statistics and probability Many Python programming concepts Pr

Adam Muhammad Klesc 1 Jan 03, 2022
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

Mask R-CNN for Object Detection and Segmentation This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bound

Matterport, Inc 22.5k Jan 04, 2023
(CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

ClassSR (CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic Paper Authors: Xiangtao Kong, Hengyuan

Xiangtao Kong 308 Jan 05, 2023
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis

O-CNN This repository contains the implementation of our papers related with O-CNN. The code is released under the MIT license. O-CNN: Octree-based Co

Microsoft 607 Dec 28, 2022
The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.

VAENAR-TTS This repo contains code accompanying the paper "VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis". Sa

THUHCSI 138 Oct 28, 2022
Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation.

PersonLab This is a Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation. The model predicts heatmaps and vari

OCTI 160 Dec 21, 2022
Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT)

Complete-IoU Loss and Cluster-NMS for Improving Object Detection and Instance Segmentation. Our paper is accepted by IEEE Transactions on Cybernetics

290 Dec 25, 2022