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2017_global_ai_hackathon

ansatz-based-imaginary-time-evolution

Ansatz-based imaginary time evolution method for quantum computing, implemented using mindquantum

bda_py_demos

Bayesian Data Analysis demos for Python

biligrab-danmaku2ass

Play videos on Bilibili.com with Danmaku2ASS

bpl_python

Bayesian program learning with python

cuda_examples.jl

some useful showcase for the usage of CUDA.jl

darts.jl

personal trial of Differentiable Architecture Search

datasets

handle datasets such as mnist, cifar, coil20, fer2013 and so on.

dlab-public

Code for the paper "DLAB - Deep learning methods for structure-basedvirtual screening of antibodies"

dockerhelper.jl

Easy way to build a docker image

easy-union-find

union find in haskell, based on ST Monad, made it easy to use

easyformat.jl

"{}".format rules made string/path formatting easy in julia

easyppl

`Haskell` implementation of probabilistic programming languages(`PPL`), make it easy to use && easy to follow

egg-rise

Exprimenting with egg (https://github.com/egraphs-good/egg) for Rise (https://github.com/rise-lang)

egg_quantum

egg is a flexible, high-performance e-graph library (this fork is for quantum gate compile optimizer)

egraphs.jl

faiss

A library for efficient similarity search and clustering of dense vectors.

fusionnet-pytorch

FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

genred.jl

general reduction kernel motivated by keops

gpu_boost_toolset

toolset for data processing with gpu boosting

hybrid-quantum-tensor-network

This project implement the hybrid tensor network idea for quantum computing

kmeans_pytorch

pytorch implementation of basic kmeans algorithm(lloyd method with forgy initialization) with gpu support

maybetensor.jl

MaybeTensor is just nested vectors, where every level could be Nothing or some value. Note that performance(speed) is not the core target of this project, but the flexibility. I actually tried some functional programming idea, such as Maybe Monad in this project.

monad-computation-learn

This is how I decompose, analyze and reproduce the packages related to the idea of letting Monad handle computational effects (e.g. qio-haskell and monad-bayes).

monadinterface.jl

Interface for Monad utility.

monads.jl

Monadic expressions and sequences for Julia

naivetensor

naive implementation of tensor data structure in `Haskell`

nestedvector.jl

Vector{Vector{...}} style is convenient. Representing Multi-dimentional Array in a nested vector form.

neural_net_design

omeinsum.jl

One More Einsum for Julia! With runtime order-specification and high-level adjoints for AD