NIPS Workshop on Machine Learning Open Source Software, Whistler 2008

NIPS Workshop on Machine Learning Open Source Software, Whistler 2008

17 Lectures · Dec 12, 2008

About

We believe that the wide-spread adoption of open source software policies will have a tremendous impact on the field of machine learning. The goal of this workshop is to further support the current developments in this area and give new impulses to it. Following the success of the inaugural NIPS-MLOSS workshop held at NIPS 2006, the Journal of Machine Learning Research (JMLR) has started a new track for machine learning open source software initiated by the workshop's organizers. Many prominent machine learning researchers have co-authored a position paper advocating the need for open source software in machine learning. Furthermore, the workshop's organizers have set up a community website mloss.org where people can register their software projects, rate existing projects and initiate discussions about projects and related topics. This website currently lists 132 such projects including many prominent projects in the area of machine learning.

The main goal of this workshop is to bring the main practitioners in the area of machine learning open source software together in order to initiate processes which will help to further improve the development of this area. In particular, we have to move beyond a mere collection of more or less unrelated software projects and provide a common foundation to stimulate cooperation and interoperability between different projects. An important step in this direction will be a common data exchange format such that different methods can exchange their results more easily.

More information about workshop - http://mloss.org/workshop/nips08/

Related categories

Uploaded videos:

video-img
09:42

Introduction and overwiew of the Machine Learning Open Source Software workshop

Sören Sonnenburg

Dec 20, 2008

 · 

4103 Views

Introduction
video-img
46:44

Octave

John W. Eaton

Dec 20, 2008

 · 

14646 Views

Lecture
video-img
26:52

Torch

Ronan Collobert

Dec 20, 2008

 · 

4764 Views

Lecture
video-img
17:52

Shark

Tobias Glasmachers

Dec 20, 2008

 · 

5935 Views

Lecture
video-img
20:20

Kernlab

Alexandros Karatzoglou

Dec 20, 2008

 · 

10711 Views

Lecture
video-img
12:00

Machine Learning Py (mlpy)

Davide Albanese

Dec 20, 2008

 · 

9710 Views

Lecture
video-img
13:19

MDP – Modular toolkit for Data Processing

Tiziano Zito

Dec 20, 2008

 · 

4789 Views

Lecture
video-img
36:05

What is a good mloss project

Dec 20, 2008

 · 

2909 Views

Debate
video-img
47:08

Matplotlib

John D. Hunter

Dec 20, 2008

 · 

35700 Views

Invited Talk
video-img
21:11

Disco

Ville H. Tuulos

Dec 20, 2008

 · 

4275 Views

Lecture
video-img
15:02

Nieme

Francis Maes

Dec 20, 2008

 · 

3285 Views

Lecture
video-img
14:04

libDAI

Joris Mooij

Dec 20, 2008

 · 

6385 Views

Lecture
video-img
11:04

BCPy2000

Jeremy Hill

Dec 20, 2008

 · 

8056 Views

Lecture
video-img
10:00

Model Monitor

Troy William Raeder

Dec 20, 2008

 · 

3044 Views

Lecture
video-img
19:42

RL Glue and Codecs Glue

Brian Tanner

Dec 20, 2008

 · 

6093 Views

Lecture
video-img
19:59

Experiment Databases for Machine Learning / BenchMarking Via Weka

Peter Reutemann

Dec 20, 2008

 · 

6259 Views

Lecture
video-img
29:22

Reproducible research

Sören Sonnenburg,

Mikio Braun,

Cheng Soon Ong

Dec 20, 2008

 · 

3051 Views

Debate