muxspace,Brian Lee Yung Rowe,github

bson_erlang

BSON documents in Erlang, see bsonspec.org

bunny_farm

AMQP erlang client wrapper library using the RabbitMQ libraries

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/.

cuny_msda_is622

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

diabetes

doppelganger

Replication for riak

ebola.sitrep

Consolidated, machine-readable ebola situation reports

erlware_commons

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

facial_expressions

A set of images for classifying facial expressions

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

influxdb

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

jsonerl

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

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

lager

A logging framework for Erlang/OTP

misultin

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

neuraltalk2

Efficient Image Captioning code in Torch, runs on GPU

pyramid_cookbook

Pyramid cookbook recipes (documentation)

rabbit_common

This is a stripped down distribution of the rabbitmq-server code required by the rabbitmq-erlang-client for use as a rebar dep. YOU SHOULD NEVER FORK/EDIT THIS CODE IT IS JUST TO SATISFY GEN_BUNNY's REBAR DEPENDENCY.