Merge pull request #1 from neurodata/staging

Staging
david-z-shi · Oct 16, 2020 · b19f768 · b19f768
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diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -4,7 +4,10 @@ Contributing to ProgLearn
 (adopted from scikit-learn)
 
 The latest contributing guide is available in the repository at
-`docs/contributing.rst`
+`docs/contributing.rst`, or online at:
+
+[https://proglearn.neurodata.io/contributing.html](https://proglearn.neurodata.io/contributing.html)
+
 
 There are many ways to contribute to ProgLearn, with the most common ones
 being contribution of code or documentation to the project. Improving the
@@ -23,6 +26,13 @@ up" on issues that others reported and that are relevant to you. It also helps
 us if you spread the word: reference the project from your blog and articles,
 link to it from your website, or simply star it in GitHub to say "I use it".
 
+Quick links
+-----------
+
+* [Submitting a bug report or feature request](http://proglearn.neurodata.io/contributing.html#submitting-a-bug-report-or-a-feature-request)
+* [Contributing code](http://proglearn.neurodata.io/contributing.html#contributing-code)
+* [Coding guidelines](http://proglearn.neurodata.io/contributing.html#coding-guidelines)
+
 Code of Conduct
 ---------------
 

diff --git a/README.md b/README.md
@@ -1,80 +1,21 @@
 # ProgLearn
 
 [![Build Status](https://travis-ci.org/neurodata/ProgLearn.svg?branch=main)](https://travis-ci.org/neurodata/ProgLearn)
-[![codecov](https://codecov.io/gh/neurodata/ProgLearn/branch/main/graph/badge.svg)](https://codecov.io/gh/neurodata/ProgLearn)
+[![codecov](https://codecov.io/gh/neurodata/ProgLearn/branches/main/graph/badge.svg)](https://codecov.io/gh/neurodata/ProgLearn)
 [![PyPI version](https://img.shields.io/pypi/v/proglearn.svg)](https://pypi.org/project/proglearn/)
 [![arXiv shield](https://img.shields.io/badge/arXiv-2004.12908-red.svg?style=flat)](https://arxiv.org/abs/2004.12908)
 [![License](https://img.shields.io/badge/License-MIT-blue)](https://opensource.org/licenses/MIT)
 
-`proglearn` (**Prog**ressive **Learn**ing) is a package for exploring and using progressive learning algorithms developed by the [neurodata group](https://neurodata.io).
+`ProgLearn` (**Prog**ressive **Learn**ing) is a package for exploring and using progressive learning algorithms developed by the [neurodata group](https://neurodata.io).
 
-- [Overview](#overview)
-- [Documentation](#documentation)
-- [System Requirements](#system-requirements)
-- [Installation Guide](#installation-guide)
-- [Contributing](#contributing)
-- [License](#license)
-- [Issues](#issues)
+- **Installation Guide:** [http://proglearn.neurodata.io/install.html](http://proglearn.neurodata.io/install.html)
+- **Documentation:** [http://proglearn.neurodata.io](http://proglearn.neurodata.io)
+- **Tutorials:** [http://proglearn.neurodata.io/tutorials.html](http://proglearn.neurodata.io/tutorials.html)
+- **Source Code:** [http://proglearn.neurodata.io/reference/index.html](http://proglearn.neurodata.io/reference/index.html)
+- **Issues:** [https://github.com/neurodata/proglearn/issues](https://github.com/neurodata/proglearn/issues)
+- **Contribution Guide:** [http://proglearn.neurodata.io/contributing.html](http://proglearn.neurodata.io/contributing.html)
 
-# Overview
-The natural process of biological learning involves progressive acquisition of new information developing on past knowledge and experiences, which often leads to a performance improvement on a given task. Learning a second language, for instance, is associated with higher performance in an individual’s native language compared to that of monolinguals. In classical machine learning, the process usually begins from the state of <i>tabula rasa</i>, zero knowledge, and is optimized for a single task. The issues arise when the system is sequentially optimized for multiple tasks exhibiting “catastrophic forgetting,” diminishing performance of previously learned tasks. One of the current limitations of artificial intelligence revolves around this inability to transfer knowledge. <br><br>
-The progressive learning package utilizes representation ensembling algorithms to sequentially learn a representation for each task and ensemble both old and new representations for all future decisions. Here, two complementary representation ensembling algorithms based on decision forests (Lifelong Forest) and deep networks (Lifelong Network) demonstrate forward and backward knowledge transfer of tasks on multiple real datasets, including both vision and language applications.
-
-# Documentation
-
-
-# System Requirements
-## Hardware requirements
-`proglearn` package requires only a standard computer with enough RAM to support the in-memory operations.
-
-## Software requirements
-### OS Requirements
-This package is supported for *Linux* and *macOS*. The package has been tested on the following systems:
-+ Linux: Ubuntu 16.04
-+ macOS: Mojave (10.14.1)
-+ Windows: 10
-
-### Python Requirements
-This package is written for Python3. Currently, it is supported for Python 3.6 and 3.7.
-
-### Python Dependencies
-`proglearn` mainly depends on the Python scientific stack.
-```
-keras>=2.3.1
-tensorflow>=1.19.0
-scikit-learn>=0.22.0
-scipy==1.4.1
-numpy<1.19
-joblib>=0.14.1
-```
-
-# Installation Guide
-## Install from pip
-```
-pip install proglearn
-```
-
-## Install from Github
-```
-git clone https://github.com/neurodata/ProgLearn.git
-cd ProgLearn
-python3 setup.py install
-```
-
-# Contributing
-We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/neurodata/ProgLearn/blob/main/CONTRIBUTING.md) before making a pull request. Our
-[issues](https://github.com/neurodata/ProgLearn/issues) page is full of places we could use help!
-If you have an idea for an improvement not listed there, please
-[make an issue](https://github.com/neurodata/ProgLearn/issues/new) first so you can discuss with the
-developers.
-
-# License
-This project is covered under the [MIT License](https://github.com/neurodata/ProgLearn/blob/main/LICENSE).
-
-# Issues
-We appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our [issues](https://github.com/neurodata/ProgLearn/issues) page if you have questions or ideas.
-
-# Citing ProgLearn
-If you find ProgLearn useful in your work, please cite the package via the [progressive-learning paper](https://arxiv.org/pdf/2004.12908.pdf)
-
-> Vogelstein JT, Helm HS, Mehta RD, Dey J, Yang W, Tower B, LeVine W, Larson J, White C, Priebe CE. A general approach to progressive learning. arXiv preprint arXiv:2004.12908. 2020 Apr 27.
+Some system/package requirements:
+- **Python**: 3.6+
+- **OS**: All major platforms (Linux, macOS, Windows)
+- **Dependencies**: keras, scikit-learn, scipy, numpy, joblib
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