From 8087caa4abee14de85c379b76a8adf27203a9ab6 Mon Sep 17 00:00:00 2001 From: metastableB Date: Mon, 11 Dec 2017 11:04:56 +0530 Subject: [PATCH 1/2] Updating readme with wiki links --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 535c9594..b40e0f4f 100644 --- a/README.md +++ b/README.md @@ -8,11 +8,11 @@ This repository contains two such algorithms **Bonsai** and **ProtoNN** that shi For details, please see the ICML'17 publications on [Bonsai](publications/Bonsai.pdf) and [ProtoNN](publications/ProtoNN.pdf) algorithms. -Initial Code Contributors: [Chirag Gupta](https://github.com/AIgen), [Aditya Kusupati](https://adityakusupati.github.io/), [Ashish Kumar](https://ashishkumar1993.github.io/), and [Harsha Simhadri](http://harsha-simhadri.org). +Initial Code Contributors: [Chirag Gupta](https://aigen.github.io/), [Aditya Kusupati](https://adityakusupati.github.io/), [Ashish Kumar](https://ashishkumar1993.github.io/), and [Harsha Simhadri](http://harsha-simhadri.org). We welcome contributions, comments and criticism. For questions, please [email Harsha](mailto:harshasi@microsoft.com). -[People](http://harsha-simhadri.org/EdgeML/People/) who have contributed to this [project](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/). +[People](https://github.com/Microsoft/EdgeML/wiki/People/) who have contributed to this [project](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/). ### Requirements * Linux: From 96c24e00c8efda0baf72de45e9204e61e82d7b15 Mon Sep 17 00:00:00 2001 From: metastableB Date: Mon, 11 Dec 2017 11:08:05 +0530 Subject: [PATCH 2/2] Adding link to new wiki page --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b40e0f4f..95a36aec 100644 --- a/README.md +++ b/README.md @@ -6,8 +6,8 @@ Machine learning models for edge devices need to have a small footprint in terms This repository contains two such algorithms **Bonsai** and **ProtoNN** that shine in this setting. These algorithms can train models for classical supervised learning problems with memory requirements that are orders of magnitude lower than other modern ML algorithms. The trained models can be loaded onto edge devices such as IoT devices/sensors, and used to make fast and accurate predictions completely offline. -For details, please see the ICML'17 publications on [Bonsai](publications/Bonsai.pdf) and [ProtoNN](publications/ProtoNN.pdf) algorithms. - +For details, please see our [wiki page](https://github.com/Microsoft/EdgeML/wiki/) and our ICML'17 publications on [Bonsai](publications/Bonsai.pdf) and [ProtoNN](publications/ProtoNN.pdf) algorithms. + Initial Code Contributors: [Chirag Gupta](https://aigen.github.io/), [Aditya Kusupati](https://adityakusupati.github.io/), [Ashish Kumar](https://ashishkumar1993.github.io/), and [Harsha Simhadri](http://harsha-simhadri.org). We welcome contributions, comments and criticism. For questions, please [email Harsha](mailto:harshasi@microsoft.com).