Code accompanying the paper Shared Independent Component Analysis for Multi-subject Neuroimaging

Related tags

Deep LearningShICA
Overview

ShICA

CircleCI

Code accompanying the paper Shared Independent Component Analysis for Multi-subject Neuroimaging

Install

Move into the ShICA directory cd ShICA

Install ShICA pip install -e .

Reproduce synthetic experiments in Figure 2

Move into the experiments directory cd experiments

Run the bash script to produce results (should take approximately 3 minutes on a modern laptop) bash run_all.bash

Move into the plotting directory cd plotting

Run the bash script to produce figures from the results bash plot_all.bash

Figures are available in the figures directory.

Performances on Gaussian sources:

Full non Gaussian

Performances on non Gaussian sources:

Full Gaussian

Performances when some sources are Gaussian and some non-Gaussian:

Semi Gaussian

Note The current implementation uses only 10 seeds and 4 different number of samples in the curves so that computation time is low even on a laptop. In order to obtain exactly the same curves as in the paper you should modify the files rotation.py, full_nongaussian.py and semigaussian.py in the experiments directory so that

num_points = 20
seeds = np.arange(40)
ns = np.logspace(2, 5, num_points)

Documentation

https://hugorichard.github.io/ShICA/index.html

Owner
PhD Candidate Machine Learning
SSL_SLAM2: Lightweight 3-D Localization and Mapping for Solid-State LiDAR (mapping and localization separated) ICRA 2021

SSL_SLAM2 Lightweight 3-D Localization and Mapping for Solid-State LiDAR (Intel Realsense L515 as an example) This repo is an extension work of SSL_SL

Wang Han 王晗 1.3k Jan 08, 2023
Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization

YOLaT-VectorGraphicsRecognition This repository is the official PyTorch implementation of our NeurIPS-2021 paper: Recognizing Vector Graphics without

Microsoft 49 Dec 20, 2022
An official implementation of MobileStyleGAN in PyTorch

MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis Official PyTorch Implementation The accompanying videos c

Sergei Belousov 602 Jan 07, 2023
Exploring Image Deblurring via Blur Kernel Space (CVPR'21)

Exploring Image Deblurring via Encoded Blur Kernel Space About the project We introduce a method to encode the blur operators of an arbitrary dataset

VinAI Research 118 Dec 19, 2022
PyTorch code for the "Deep Neural Networks with Box Convolutions" paper

Box Convolution Layer for ConvNets Single-box-conv network (from `examples/mnist.py`) learns patterns on MNIST What This Is This is a PyTorch implemen

Egor Burkov 515 Dec 18, 2022
Probabilistic Tensor Decomposition of Neural Population Spiking Activity

Probabilistic Tensor Decomposition of Neural Population Spiking Activity Matlab (recommended) and Python (in developement) implementations of Soulat e

Hugo Soulat 6 Nov 30, 2022
An end-to-end regression problem of predicting the price of properties in Bangalore.

Bangalore-House-Price-Prediction An end-to-end regression problem of predicting the price of properties in Bangalore. Deployed in Heroku using Flask.

Shruti Balan 1 Nov 25, 2022
Combining Diverse Feature Priors

Combining Diverse Feature Priors This repository contains code for reproducing the results of our paper. Paper: https://arxiv.org/abs/2110.08220 Blog

Madry Lab 5 Nov 12, 2022
CVPR 2020 oral paper: Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax.

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax ⚠️ Latest: Current repo is a complete version. But we delet

FishYuLi 341 Dec 23, 2022
An excellent hash algorithm combining classical sponge structure and RNN.

SHA-RNN Recurrent Neural Network with Chaotic System for Hash Functions Anonymous Authors [摘要] 在这次作业中我们提出了一种新的 Hash Function —— SHA-RNN。其以海绵结构为基础,融合了混

Houde Qian 5 May 15, 2022
Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Introduction This repository contains my unofficial reimplementation of the standard ECAPA-TDNN, which is the speaker recognition in VoxCeleb2 dataset

Tao Ruijie 277 Dec 31, 2022
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Introduction This is a Python package available on PyPI for NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pyto

Artit 'Art' Wangperawong 5 Sep 29, 2021
Best Practices on Recommendation Systems

Recommenders What's New (February 4, 2021) We have a new relase Recommenders 2021.2! It comes with lots of bug fixes, optimizations and 3 new algorith

Microsoft 14.8k Jan 03, 2023
A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

Tom 50 Dec 16, 2022
Easy genetic ancestry predictions in Python

ezancestry Easily visualize your direct-to-consumer genetics next to 2500+ samples from the 1000 genomes project. Evaluate the performance of a custom

Kevin Arvai 38 Jan 02, 2023
A Low Complexity Speech Enhancement Framework for Full-Band Audio (48kHz) based on Deep Filtering.

DeepFilterNet A Low Complexity Speech Enhancement Framework for Full-Band Audio (48kHz) based on Deep Filtering. libDF contains Rust code used for dat

Hendrik Schröter 292 Dec 25, 2022
Self-supervised learning (SSL) is a method of machine learning

Self-supervised learning (SSL) is a method of machine learning. It learns from unlabeled sample data. It can be regarded as an intermediate form between supervised and unsupervised learning.

Ashish Patel 4 May 26, 2022
2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup)智能人机交互自然语言理解赛道第二名参赛解决方案

2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup) 智能人机交互自然语言理解赛道第二名解决方案 比赛网址: CCIR-Cup-智能人机交互自然语言理解 1.依赖环境: python==3.8 torch==1.7.1+cu110 numpy==1.19.2 transformers=

JinXiang 22 Oct 29, 2022
Code for database and frontend of webpage for Neural Fields in Visual Computing and Beyond.

Neural Fields in Visual Computing—Complementary Webpage This is based on the amazing MiniConf project from Hendrik Strobelt and Sasha Rush—thank you!

Brown University Visual Computing Group 29 Nov 30, 2022
Using LSTM to detect spoofing attacks in an Air-Ground network

Using LSTM to detect spoofing attacks in an Air-Ground network Specifications IDE: Spider Packages: Tensorflow 2.1.0 Keras NumPy Scikit-learn Matplotl

Tiep M. H. 1 Nov 20, 2021