Saturday, June 2, 2007

ICML 2007

The 24th Annual International Conference on Machine Learning is just around the corner and will be held in conjunction with the 2007 International Conference on Inductive Logic Programming at Oregon State University in Corvallis, Oregon.

See the list of accepted papers at:

An interesting yet amusing view of writing job advertisements

I read an interesting and amusing post in use Perl about job advertisements

It says that you want to hire people that fit. It also says that the audience splits into the following four groups of people:
1. Those that qualify: have the necessary required skills and knowledge and that can do the job
2. Those who are unqualified and are honest about it
3. Those that think that they qualify but they don't
4. Those that do not qualify but are not honest about it and lie

This looks like a partition that should make life easy: you want to hire people from the first group and not from any other group.

Two problems are being raised:
One that those that qualify most probably already are working. This means that any advertisement targeted at them should be clear about why switching jobs is worth doing for them?
The second problem is that it is hard to filter out the people from groups 3 and 4.

The post further tries to give some advice about the proper way to advertise your "wanted ad".

I like that posting very much and enjoyed reading it.

google face search

Try adding to the query string in the resulting search URL in your google search the following: &imgtype=face


I played with this a few times now and it is kind-off disappointing... this feature has some serious maturing to do :-)

Photographing children in the kindergarten

I started a new thread about Photographing children in kindergarten.
My goad i to get tips regarding the approach, lighting, composition, technical issues and the general approach.

The discussion is in Hebrew in a Tapuz forum:

JINR -- publish your negative results on NLP and ML

JINR is an electronic journal, which brings to the fore research in Natural Language Processing and Machine Learning that uncovers interesting negative results.

They idea is that while we see a lot of publications about successful attempts, it is not clear which paths are not promising directions.

Much can be learned by analysing why some ideas, while intuitive and plausible, do not work. The importance of counter-examples for disproving conjectures is already well known.

Negative results may point to interesting and important open problems. Knowing directions that lead to dead-ends in research can help others avoid replicating paths that take them nowhere. This might accelerate progress or even break through walls!