ntienvu Goto Github PK
Name: Vu Nguyen
Type: User
Company: Amazon
Bio: Machine Learning Researcher
Twitter: nguyentienvu
Location: Adelaide, Australia
Blog: vu-nguyen.org
Name: Vu Nguyen
Type: User
Company: Amazon
Bio: Machine Learning Researcher
Twitter: nguyentienvu
Location: Adelaide, Australia
Blog: vu-nguyen.org
Abnormality Detection using PCA approach
Source code for abnormal detection on MIT video surveillance dataset using Nonnegative Matrix Factorization
Source code for Bayesian Nonparametric Multi-label Classification ACML 2016
😎 A curated list of awesome GitHub Profile READMEs 📝
Reinforcement learning resources curated
An interactive introduction to bayesian non-parametrics
Starter kit for the black box optimization challenge at Neurips 2020
Release code for Bayesian Optimization for Iterative Learning (BOIL) at NeurIPS2020
Causal inference is a critical task in various fields such as healthcare,economics, marketing and education. Recently, there have beensignificant advances through the application of machine learningtechniques, especially deep neural networks. Unfortunately, to-datemany of the proposed methods are evaluated on different (data,software/hardware, hyperparameter) setups and consequently it isnearly impossible to compare the efficacy of the available methodsor reproduce results presented in original research manuscripts.In this paper, we propose a causal inference toolbox (CauseBox)that addresses the aforementioned problems. At the time of thewriting, the toolbox includes seven state of the art causal inferencemethods and two benchmark datasets. By providing convenientcommand-line and GUI-based interfaces, theCauseBoxtoolboxhelps researchers fairly compare the state of the art methods intheir chosen application context against benchmark datasets.
Bayesian Optimisation over Multiple Continuous and Categorical Inputs (CoCaBO)
Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2.
Implementations from the free course Deep Reinforcement Learning with Tensorflow
A series of tutorial notebooks on denoising diffusion probabilistic models in PyTorch
Deep Reinforcement Learning for Efficient Measurement of Quantum Devices
Experiment code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"
VI implementation for inference of the IBP
Released code for ICDM 2016 Budgeted Batch Bayesian Optimization
Released code for ICDM 2016 One-pass Logistic Regression
Filtering Bayesian Optimization (FBO) in Weakly Specified Search Space
Release code for ICML2020 Knowing The What But Not The Where in Bayesian Optimization
KWN Modeling for Increased Efficiency of Al-Sc Precipitation Strengthening
LargeSampleAsymptotic_BNP_NIPS_AABI_2015
A collection of machine learning examples and tutorials.
Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization
Companion webpage to the book "Mathematics For Machine Learning"
Matlab code for Nonparametric Budgeted SGD for classification and regression (AISTATS 2016)
Bayesian Structure Adaptation for Continual Learning
Visualize optimization algorithms in MATLAB.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.