harry-zhou Goto Github PK

dlsys-course.github.io

Deep learning system course

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

drl

Deep Reinforcement Learning

dynet

DyNet: The Dynamic Neural Network Toolkit

econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

emu

The write-once-run-anywhere GPGPU library for Rust

energycircuittheory-energyflowcalculation

数值实验代码 for 《综合能源系统分析的统一能路理论(三)：稳态与动态潮流计算》

external-attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

fastor

A lightweight high performance tensor algebra framework for modern C++

firacode

Free monospaced font with programming ligatures

flashlight

A C++ standalone library for machine learning

football

Check out the new game server:

gcc

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

gcforest

Python implementation of deep forest method : gcForest

gcforest-1

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gem

gflags

The gflags package contains a C++ library that implements commandline flags processing. It includes built-in support for standard types such as string and the ability to define flags in the source file in which they are used. Online documentation available at:

glog

C++ implementation of the Google logging module

gmi

Graph Representation Learning via Graphical Mutual Information Maximization

gnnpapers

Must-read papers on graph neural networks (GNN)

gobigger

Come & try Decision-Intelligence version of "Agar"! Gobigger could also help you with multi-agent decision intelligence study.

gobigger-challenge-2021

Interested in multi-agents? The 1st Go-Bigger Multi-Agent Decision Intelligence Challenge is coming and a big bonus is waiting for you!

gpt-gnn

Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"

grace

[GRL+ @ ICML 2020] PyTorch implementation for "Deep Graph Contrastive Representation Learning" (https://arxiv.org/abs/2006.04131v2)

grakel

A scikit-learn compatible library for graph kernels

graph-bert

Source code of Graph-Bert

graph-databases-testsuite

Docker Images, installation scripts, and testing & benchmarking suite for Graph Databases

graph-infoclust-gic

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

graph-kernels

Graph kernels