muxspace

facial_expressions

A set of images for classifying facial expressions

bunny_farm

AMQP erlang client wrapper library using the RabbitMQ libraries

doppelganger

Replication for riak

ebola.sitrep

Consolidated, machine-readable ebola situation reports

rekon

Riak node data browser, built as a riak app

socket.io-erlang

Socket.IO server for Erlang

cuny_msda_is622

Resources for CUNY MS Data Anayltics course IS622 Big Data & Machine Learning

jsx

an evented, streaming json parser based in part on yajl. master is latest stable release, develop is the current stable codebase and prior releases are tags

jsonerl

yet another but slightly different erlang <-> json encoder/decoder

misultin

Misultin (pronounced mee-sool-téen) is an Erlang library for building fast lightweight HTTP(S) servers, which also supports websockets.

futile.any

Polymorphism for R

futile.paradigm

A functional dispatcher in R to replace S3 and S4

hemlock

A signal generation library for quantitative trading in R

lager

A logging framework for Erlang/OTP

riak_pipe

Riak Pipelines

rpandasr

Interoperability guide between pandas and R

sentinerl

Benchmarking tool for erlang

char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

climate-cooperation-competition

Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and reach climate agreements, but there is no central authority to enforce adherence to those agreements. Hence, it is critical to design negotiation and agreement frameworks that foster cooperation, allow all agents to meet their individual policy objectives, and incentivize long-term adherence. This is an interdisciplinary challenge that calls for collaboration between researchers in machine learning, economics, climate science, law, policy, ethics, and other fields. In particular, we argue that machine learning is a critical tool to address the complexity of this domain. To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks. We also describe how to use multi-agent reinforcement learning to train rational agents using RICE-N. This framework underpinsAI for Global Climate Cooperation, a working group collaboration and competition on climate negotiation and agreement design. Here, we invite the scientific community to design and evaluate their solutions using RICE-N, machine learning, economic intuition, and other domain knowledge. More information can be found on http://www.ai4climatecoop.org/.

diabetes

erlware_commons

Erlware Commons is an Erlware project focused on all aspects of reusable Erlang components.

influxdb

Docker image to run an out-of-the-box InfluxDB server

neuraltalk2

Efficient Image Captioning code in Torch, runs on GPU

pyramid_cookbook

Pyramid cookbook recipes (documentation)

rdomo

R language sdk for Domo

riak-r-client

An R client for Riak

rredis

R client for Redis

sdss_2020

stanford_alpaca

Code and documentation to train Stanford's Alpaca models, and generate the data.

zeomancer_web