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spare-beyesian

Brain Computer Interface(BCI) data is transformed and banked by BCI from human brain action to.Sparse Bayesian learning is a machine learning method.Its high efficiency of calculation meets the requirement of the analysis of the Brain Computer Interface Data.Here we use Sparse Bayesian linear Discriminant Analysis(SBDA) and its variant as models to classify the electroencephalogram (EEG) tested in P300 dataset from the BCI competition 2004.Through a series of pre-process including bandpass filtering , normalized , time trial extraction，downsample，winsorize，etc , their data recognized by SBDA achieves the accuracy achievable with Support Vector Machine(SVM) .Moreover,the computational complexity of SVM is much higher than SBDA and its variant.

sparsediscrim

Sparse and Regularized Discriminant Analysis in R

sparsekos

This is a Git repository for sparse kernel optimal scoring (sparse KOS).

sparselda

:exclamation: This is a read-only mirror of the CRAN R package repository. sparseLDA — Sparse Discriminant Analysis. Homepage: http://www.imm.dtu.dk/~lhc, https://github.com/topepo/sparselda

stackgan

t3c_tensor_completion

tensor-train tensor completion (T3C), which is based on tt decomposition and gradient descent.

tensor-completion-tensor-recovery

tensor completion/tensor recovery from Gaussian measurements by tensor nuclear norm minimization

tensor-robust-pca-and-tensor-completion-under-linear-transform

Tensor robust PCA (TRPCA) and tensor completion based on tensor nuclear norm under linear transform

tensor-robust-principal-component-analysis-trpca

Tensor Robust Principal Component Analysis (TRPCA) based on a new tensor nuclear norm

text-to-image

Generating images from text using AttnGAN. (Inference only)

trlrf

Tensor ring low-rank factors (AAAI 2019)

usrnet

Deep Unfolding Network for Image Super-Resolution (CVPR, 2020) (PyTorch)

vintage_games

This repo contains the zip file of nintendo emulators and few nintendo games (ROM files). An instruction video is also present.

xnor-net

ImageNet classification using binary Convolutional Neural Networks