Lena Huang's repositories
mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
bioconda-recipes
Conda recipes for the bioconda channel.
linnabrown.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
daily-llama
meta's LLMA-2 based chat framework, optimized for latest news contents.
plink-ng
A comprehensive update to the PLINK association analysis toolset. Beta testing of the first new version (1.90), focused on speed and memory efficiency improvements, is finishing up. Development is now focused on building out support for multiallelic, phased, and dosage data in PLINK 2.0.
snpnet
snpnet: Fast and scalable lasso/elastic-net solver for large SNP data
GMVC
minimum vertex cover algorithm applied to human genetic kinship data (genetic_minimum_vertex_cover)
stable-diffusion-webui
Stable Diffusion web UI
gin
Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.
COMP790-melody
Those readme contains our generated polyphonic MIDI format data from COMP 790 Lecture. Those data are in midi format and you can listen to it.
Comp790-166-Comp-Bio
Computational Biology- Spring 2021
bash_tricks
Bash files that may useful in the future.
dotfiles
cd && git clone --bare https://github.com/craffel/dotfiles.git .dotfiles && git --git-dir=.dotfiles --work-tree=. checkout
gwas-lecture
Lectures on Genome Wide Association Studies (GWAS) using Python, Limix and Jupyter Noteboos
pretty-midi
Utility functions for handling MIDI data in a nice/intuitive way.
glmnetPlus
glmnet package with initialization
saveSvgAsPng
Save SVGs as PNGs from the browser.
samtools
Tools (written in C using htslib) for manipulating next-generation sequencing data
vision
Datasets, Transforms and Models specific to Computer Vision
hicplus_revised
This is revised scripts of hicplus
autogroup
Split your big directory [1,2,...,n] into pieces of small directory[1:[1,2,3], 2:[4,5,6], ... n[n+3, n+4, n+5]]
variational-continual-learning
Implementation of the variational continual learning method