Profile cover photo
Profile photo
Cheng Soon Ong
245 followers -
Curious. Machine learning for Scientific Discovery. I choose open science.
Curious. Machine learning for Scientific Discovery. I choose open science.

245 followers
About
Communities and Collections
View all
Posts

Post has shared content
Add a comment...

Post has attachment

Post has shared content
Add a comment...

Post has attachment
10 days left for submitting something to our ICML workshop in Sydney on human in the loop machine learning.
https://machlearn.gitlab.io/hitl2017/call-for-contributions/
Add a comment...

Post has shared content
Students, start your engineerings! Today we announce who has been accepted for Google Summer of Code 2017 -- our biggest year yet:
Students, Start Your Engineerings!
Students, Start Your Engineerings!
opensource.googleblog.com
Add a comment...

Post has shared content
Evaluating an OA journal you haven't heard of.

A colleague was just invited to join the editorial board of an OA journal he hadn't heard of, and asked my advice on how to evaluate it. Here's an anonymized version of my reply.

.....

I don't know [Journal or Publisher]. But I'd start by checking to see whether [Journal] is listed in the Directory of Open Access Journals (DOAJ)<http://doaj.org/>, which tries to include all honest, peer-reviewed OA journals and exclude the dishonest ones.

I'd also check to see whether [Publisher] belongs to the Open Access Scholarly Publishers Association (OASPA) <http://oaspa.org/>, which excludes publishers who do not live up to its code of ethics.

Some honest, high-quality OA journals are not yet listed in the DOAJ, and some honest, high-quality OA publishers do not yet belong to OASPA. But we should encourage them to apply. If your investigation of [Journal and Publisher] doesn't turn up evidence you trust one way or another, then follow the rule to avoid journals that aren't listed in the DOAJ and avoid publishers who aren't members of OASPA. Don't hesitate to tell them that this is your criterion. (For example, "I'll join your board once [Journal] is listed in the DOAJ and [Publisher] joins OASPA.") That will give them an incentive to join, and live up to DOAJ-OASPA standards.

I'd also consult the criteria at Think-Check-Submit <http://thinkchecksubmit.org/>, and the reviews at JournalReviewer <http://journalreviewer.org/> and Journalysis <http://www.journalysis.org/>, and Quality Open Access Market <http://www.qoam.eu/>.

Since this is a journal in your field, look at the names of people on the editorial board. Do you recognize and respect them? Above all, read some of the journal's articles, and network with trusted colleagues to do the same. Are the articles good, by your standards? Would you be proud or embarrassed to be associated with them?

.....

I say a bit more in my online handout, How to make your own work open access.
http://bit.ly/how-oa

Add a comment...

Post has shared content
Consider submitting your best machine learning research work to NIPS this year. All details below. Deadline is May 19th.
Add a comment...

Post has attachment
The Bandwagon
(using in the words of Claude Shannon, 1956)

Machine Learning has, in the last few years, become something of a scientific bandwagon. Starting as a technical tool for the computer scientist, it has received an extraordinary amount of publicity in the popular as well as the scientific press. In part, this has been due to connections with such fashionable fields computing machines, cybernetics, and automation; and in part, to the novelty of the subject matter. As a consequence, it has perhaps been ballooned to an importance beyond its actual accomplishments. Our fellow scientists in many different fields, attracted by the fanfare and by the new avenues opened to scientific analysis, are using these ideas in their own problems. Applications are being made to biology, psychology, linguistics, fundamental physics, economics, the theory of organisation, and many others. In short, machine learning is currently partaking of a somewhat heady draught of general popularity.

Although this wave of popularity is certainly pleasant and exciting for those of us working in the field, it carries at the same time an element of danger. While we feel that machine learning is indeed a valuable tool in providing fundamental insights into the nature of computing problems and will continue to grow in importance, it is certainly no panacea for the computer scientist or, a fortiori, for anyone else. Seldom do more than a few of natures' secrets give way at one time. It will be all too easy for our somewhat artificial prosperity to collapse overnight when it is realised that the use of a few exciting words like deep learning, artificial intelligence, data science, do not solve all our problems.

What can be done to inject a note of moderation in this situation? In the first place, workers in other fields should realise that the basic results of the subject are aimed in a very specific direction, a direction that is not necessarily relevant to such fields as psychology, economics, and other social sciences. Indeed, the hard core of machine learning is, essentially, a branch of mathematics and statistics, a strictly deductive system. A thorough understanding of the mathematical foundation and its computing application is surely a prerequisite to other applications. I personally believe that many of the concepts of machine learning will prove useful in these other fields -- and, indeed, some results are already quite promising -- but the establishing of such applications is not a trivial matter of translating words to a new domain, but rather the slow tedious process of hypothesis and experimental verification. If, for example, the human being acts in some situations like an ideal predictor, this is an experimental and not a mathematical fact, and as such must be tested under a wide variety of experimental situations.

Secondly, we must keep our own house in first class order. The subject of machine learning has certainly been sold, if not oversold. We should now turn our attention to the business of research and development at the highest scientific plane we can maintain. Research rather than exposition is the keynote, and our critical thresholds should be raised. Authors should submit only their best efforts, and these only after careful criticism by themselves and their colleagues. A few first rate research papers are preferable to a large number that are poorly conceived or half-finished. The latter are no credit to their writers and a waste of time to their readers. Only by maintaining a thoroughly scientific attitude can we achieve real progress in machine learning and consolidate our present position.


http://www.eoht.info/page/Shannon+bandwagon
Add a comment...

Post has shared content
Give the Earth a present: help us save climate data

We've been busy backing up climate data before Trump becomes President. Now you can help too, with some money to pay for servers and storage space.   Please give what you can at our Kickstarter campaign here:

https://www.kickstarter.com/projects/592742410/azimuth-climate-data-backup-project

If we get $5000 by the end of January, we can save this data until we convince bigger organizations to take over.   If we don't get that much, we get nothing.  That's how Kickstarter works.   Also, if you donate now, you won't be billed until January 31st.

So, please help!   It's urgent.

I will make public how we spend this money.  And if we get more than $5000, I'll make sure it's put to good use.  There's a lot of work we could do to make sure the data is authenticated, made easily accessible, and so on.

The idea

The safety of US government climate data is at risk. Trump plans to have climate change deniers running every agency concerned with climate change.  So, scientists are rushing to back up the many climate databases held by US government agencies before he takes office.

We hope he won't be rash enough to delete these precious records. But: better safe than sorry!

The Azimuth Climate Data Backup Project is part of this effort. So far our volunteers have backed up nearly 1 terabyte of climate data from NASA and other agencies. We'll do a lot more!  We just need some funds to pay for storage space and a server until larger institutions take over this task.

The team

+Jan Galkowski is a statistician with a strong interest in climate science. He works at Akamai Technologies, a company responsible for serving at least 15% of all web traffic. He began downloading climate data on the 11th of December.

• Shortly thereafter +John Baez, a mathematician and science blogger at U. C. Riverside, joined in to publicize the project. He’d already founded an organization called the Azimuth Project, which helps scientists and engineers cooperate on environmental issues.

• When Jan started running out of storage space, +Scott Maxwell  jumped in. He used to work for NASA — driving a Mars rover among other things — and now he works for Google. He set up a 10-terabyte account on Google Drive and started backing up data himself.

• A couple of days later +Sakari Maaranen joined the team. He’s a systems architect at Ubisecure, a Finnish firm, with access to a high-bandwidth connection. He set up a server, he's downloading lots of data, he showed us how to authenticate it with SHA-256 hashes, and he's managing many other technical aspects of this project.

There are other people involved too.  You can watch the nitty-gritty details of our progress here:

Azimuth Backup Project - Issue Tracker:
https://bitbucket.org/azimuth-backup/azimuth-inventory/issues

and you can learn more here:

Azimuth Climate Data Backup Project.
http://math.ucr.edu/home/baez/azimuth_backup_project/

#climateaction  
Add a comment...

Post has attachment
Like Bregman divergence? We stretched the set of functions that can be used as generators (traditionally convex) #nips2016
Add a comment...
Wait while more posts are being loaded