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convention-writing-test-js

convention for writing-testing-unit in js with jest

convnetjs

Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

copyfiles

copy files on the command line

corr_prediction_arima_lstm_hybrid

Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

cors

Node.js CORS middleware

counterfactual-recurrent-network

Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. Bica, A. M. Alaa, J. Jordon, M. van der Schaar

country-statistics

Cleanse the country statistics to encode the country vectors

covid-19-api

API Service for tracking the COVID-19

crawlwto

WTO crawler

create-lambda

Create AWS Lambda using AWS CLI

critical-supplies

crl

Causal Reinforcement Learning

cron

a cron library for go

crx_reactjs

crx_server

Serve chrome extension back-end

crypto

Pythonic implementation of basic cryptographic functions

cryptonets

CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

cs234

csupp

LaTex draft about reconstructing commodity-level supply chain

cuda_memtest

Fork of CUDA GPU memtest :eyeglasses:

cvae

List up CVAE papers

cvxpy

A Python-embedded modeling language for convex optimization problems.

d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

d3-graph-gallery

A collection of simple graphics made with D3.js

d3-in-depth

d3-scale

Encodings that map abstract data to visual representation.

d3-scale-chromatic

Sequential, diverging and categorical color scales.

d3-selection

Transform the DOM by selecting elements and joining to data.

d3graphtheory

:boom: Interactive and colorful :art: graph theory tutorials made using d3.js :zap:

dads

Code for 'Dynamics-Aware Unsupervised Discovery of Skills' (DADS). Enables skill discovery without supervision, which can be combined with model-based control.