1996lixingyu1996

CRCNet

Adaptive-Graph-Convolutional-Network

attention-MIR

Code for the KDD 2019 workshop paper

AttentionDeepMIL

Implementation of Attention-based Deep Multiple Instance Learning in PyTorch

awesome-neural-trees

Introduction, selected papers and possible corresponding codes in our review paper "A Survey of Neural Trees"

axial-deeplab

This is a PyTorch re-implementation of Axial-DeepLab (ECCV 2020 Spotlight)

B240115

Basket-Hetero

Software for paper "Bayesian Adaptive Design for Concurrent Trials Involving Biologically-Related Diseases"

Bayesian-analysis-models

OpenBUGS code together with R functions to implement Bayesian inferences for basket trials.

BlurDetection2

Blur Detection with OpenCV in Python

1996lixingyu1996/causalml

Uplift modeling and causal inference with machine learning algorithms

1996lixingyu1996/MSML

1996lixingyu1996/r-source

Read-only mirror of R source code from https://svn.r-project.org/R/, updated hourly. See the build instructions on the wiki page.

1996lixingyu1996/DynForest

Random forest with multivariate longitudinal predictors

1996lixingyu1996/survival

Survival package for R

1996lixingyu1996/CFR2M

A more efficient two-stage cross-fitted estimation procedure for the R-squared(R2)-based total mediation effect measure in high-dimensional setting

1996lixingyu1996/B240115

1996lixingyu1996/npcausal

1996lixingyu1996/mastering-shiny

Mastering Shiny: a book

1996lixingyu1996/localPP

A Bayesian Basket Trial Design Using Local Power Prior

1996lixingyu1996/mediation

R package mediation

1996lixingyu1996/pinduoduo_backdoor_detailed_report

Maybe the most detailed analysis of pdd backdoors

1996lixingyu1996/sc2-2019

Source code of Statistical Computing 2 website

1996lixingyu1996/DTFD-MIL

1996lixingyu1996/causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning

1996lixingyu1996/awesome-neural-trees

Introduction, selected papers and possible corresponding codes in our review paper "A Survey of Neural Trees"

1996lixingyu1996/randomForest

:exclamation: This is a read-only mirror of the CRAN R package repository. randomForest — Breiman and Cutler's Random Forests for Classification and Regression. Homepage: https://www.stat.berkeley.edu/~breiman/RandomForests/

1996lixingyu1996/Deep-Learning-Machine-Learning-Stock

Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders.

1996lixingyu1996/Stock_Analysis_For_Quant

Different Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau

1996lixingyu1996/stocksight

Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

1996lixingyu1996/VisualizingNDF

Official PyTorch implementation of "Visualizing the Decision-making Process in Deep Neural Decision Forest", CVPR 2019 Workshops on Explainable AI

1996lixingyu1996/moco

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

1996lixingyu1996/call-r-from-c

Small example of how to call into R code from C.

1996lixingyu1996/mae-scalable-vision-learners

A TensorFlow 2.x implementation of Masked Autoencoders Are Scalable Vision Learners

1996lixingyu1996/DeepQuantreg

Deep Learning for Quantile Regression under Right Censoring

1996lixingyu1996/Real-time-detection-for-renal-pathology

A Keras implementation of YOLOv3 (Tensorflow backend)

1996lixingyu1996/Transfer_learning_Xception_pathology

Xception_transfer_learning_model

1996lixingyu1996/Bayesian-analysis-models

OpenBUGS code together with R functions to implement Bayesian inferences for basket trials.

1996lixingyu1996/mlr3extralearners

Extra learners for use in mlr3.

1996lixingyu1996/Reading-Paper