Olga's CVPR paper

I'd like to congratulate my colleague Olga Barinova on the paper, which is accepted to CVPR international conference! To my knowledge, it is the first paper from our lab, which is accepted to a Rank 1 conference (though it was written during Olga's internship at Microsoft Research).

The paper is on multiple object retrieval. They extend the Hough transform with some graphical model, which makes the results more robust. As soon as the paper become publicly available, I'll add the link here.

UPD. (Apr 20, 2010) PDF

UPD (Jul 30, 2010) Video

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Visual Assist's Tip of the Century

Probably you know the Visual Assist plug-in for Microsoft Visual Studio, which makes C++ programming in the environment zillion times handier. The story is about the bootstrap tip that highlights the feature of restoring files. While the tip window is shown, there is a second dialog box painted in the window. Since it is centred w.r.t. the screen, you perceive it as a real dialog box. Moreover, it is the usual case when Visual Assist suggests you to load some files from backup while MSVS is loading, because it does not always shut down properly. Thus, you try to close it clicking Yes or No, but nothing happens! The box is still in its place! That was really annoying.

In the later versions the developers solved the problem. They just marked the box as the example. The nice lack and the nice solution.

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Quantum Machine Learning

Recently, Google announced their intentions to use quantum algorithms for the search. They implemented Grover's algorithm on the D-Wave chip. Whatever they say about D-Wave, we should admit that quantum computers are going to change the nature of machine learning, it is only a matter of time.

In spite of I had the university course on quantum computing, I am not able to make head or tails of it. (See my recent post about our university education) But, as far as I know, the key idea is a computer can perform exhaustive search in constant time. This means that for quantum algorithms P=NP. For example, the problem of exact MAP inference in an MRF could be efficiently solved via exhaustive search in the space of all possible assignments.

However, quantum computers return probabilistic results, so there remains a work for mathematicians. But it is the work of the different kind. In the field of MRF MAP inference, all the algorithms like loopy BP will be forgotten. Another example: Google's Hartmut Neven developed a quantum version of AdaBoost. So, machine learners, go study quantum mechanics!

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University education reform

Recently, me and my friend Andrew Korolev came up with the plan of reforming Russian higher education system.

There are some evident problems in the system one can notice even in our faculty. The most courses are obsolete (or, well, legacy), a lot of courses are not enough developed (e.g. they contain only lectures without any support), some of them are also over-theorized and not applicable in real world (or at least they don't touch upon their applications). As a result, students are not motivated, they don't attend lectures and learn everything during the two-day period prior to the exam. The grades are often lousy, but nobody cares since such grades are sufficient to continue studying. (It is difficult to fail totally, especially for non-freshmen). Such knowledge is not solid and quite useless.

We make use of the following facts. The professors are well-qualified here, the students are witty and communicate with each other and with the graduates. Also, we suppose that everyone wants get more money, which is not always hold though is pretty common.

First, we need to improve the quality of courses. The problem is how to measure the quality. We state that it is proportional to the number of students who choose the course. Since the students have enough information about the course (from the lecturers and other students) and they all want to make a magnificent career, the useful courses become popular. The professors should be paid according to the number of students who attend their courses. But students also want to save their time. So, they are likely to choose the least challenging courses, which are not useful. In order to penalize them, the course part of professor's salary should be eliminated by the fraction of positive grades students get. Thus, a professor is motivated to make a comprehensive course (to attract students) and to implement a severe grade policy (to eliminate the freebie).

However, it will never work if the students are not motivated to get the good grades. In Russia, we have 4-level grades (2 through 5; 3 is enough for the pass), and most of the students are happy with 3s. Today, there are two stimuli to get greater grades, but they are quite subtle. The first is one need almost all 5s to receive the degree with honours, but who needs that? The second is 20% increment to the scholarship, which amounts not as much as one can desire. I have all excellent grades and receive some personal Sberbank scholarship, and totally it is about 100 per month. So, this is not a stimulus.

Well, one can ask, why do the students study now, if the courses are far from perfect? The answer is the conscription. In Russia, while you keep studying, you have a delay. If you fail at an exam, you get expelled and go to the army. Obviously, nobody wants to go to the Russian Army. So, everybody can learn a bit to pass an exam. Our point is to motivate students this way: if you have lousy grades, instead of summer holidays you move to a military camp. Better grades you get, less term you should serve. Thus, students WILL get good grades! But it is hard to get them, because professors loose their money. It is kind of a dual problem.

Surely, the model is way rough and could not be applied directly. Moreover, it is too funny to be taken seriously, though, as Russian proverb says, every joke contains a bit of truth.

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