WEKA (and command line issues)

Despite the incessant pestering by a lab-mate (Moe), I never got motivated to explore WEKA. For my recent "20%"-project on discriminative-generative debate, I thought I would look into WEKA. Within days (if not, hours), now I am totally loving it. A lot of useful ML components have been implemented in this toolkit. I wish I started using it a few years ago -- may be I could have had codes for some popular topic-models, including people-LDA, and random field, including SHRF, packages added to this toolkit, at the least.
(that reminds me of the unfortunate fate of my under-development C++ library that I called TOMOLIVE -- Topic models for learning in vision .. boohoo! )

Although the Weka's Experimenter GUI provides a lot of options to play with, I could not find the knob I wanted to turn, the one that iterates over the data split size. So I decided to use the command line. With multiple parameters to pass to the classifier classes that in turn are passed to the base class (and similar things happening with split evaluators), I got completely lost in the Unix-syntax. I finally got it to work in a not-so-elegant/non-extensible fashion. I want to do something like the following:

A x (B y (C d) )

or, the base class (that implements main) takes as argument x and B, where B is specified by options y and another class C (which is further specified by argument d).

As per my knowledge, the following two ways should implement this:
1. A x B -- y C -- d
2. A x "B y \" C d \" "

But, in practice, I do not observe the same result. Perhaps, there is something amiss about these expressions. I could tweak the first expression to work for me but this format falls apart when A takes another class E (with argument specifications) in addition to x and B, as "--" appears to force a single non-leaf child on the parsing of the command line. Any suggestions?

presentation tips ? (contd)

Adding to my previous post:
* I wonder if there should be at least one "hard" slide or concept in the presentation, which only a few will get.

One common suggestion about job talks is that the presenter should include some stuff that is a little in-depth to catch attention of the people who are closely following the related line of research -- mostly, to demonstrate the hardness of the problem and the significance of the presented result. I believe similar kind of expectations can be assumed for any other presentation as well. When people say "no equations", it does not mean "absolutely no equations" -- it is rather "no equations in most of the presentation, and a couple of key equations on that `hard' part of the presentation". In other words, there should be some motivation for the audience to look at additional details after the talk. They should not leave with a feeling that they understood everything. There are definitely some negative aspects of this approach, and I have a feeling that only a few people will approve of this. But, in my opinion, this has practical implications and avoids the risk of the talk being perceived as simplistic.

Course on pattern recognition

Following up on my previous post on introductory courses for learning in vision, I am adding the link for Chris's course on Advanced Topics in Pattern Recognition at University of Rochester.


Although this course has very little to do with computer vision, I think the syllabus outlines most of the machine learning tools prevalent in learning in vision. One thing I like about this course is its breadth. This course would probably not go into the intricacies of any of the concepts/models, but it is very likely to motivate students to explore further details on their own.

My take on CNN hologram

Since every Tom, Dick, and Harry is talking about CNN hologram, I thought I would also write something about it (without parenthesizing the word hologram with quotation marks, that is). Clearly, there are two camps out there: one that is too amazed by the cool technology, and the other which thinks it was horrendous. When I saw it on TV, I oscillated between the two camps, but, eventually, unsubscribed from both of them.

The CNN hologram reminded me of the system that CMU deployed at Superbowl in 2001. Prof. Takeo Kanade gave a talk at CVPR'06 in NYC. I was totally amazed by learning the details of this system: hundreds of cameras installed all around the field, miles of wires, numerous computers -- all synchronized to produce the cool technology. A true engineering marvel! It might be a very far stretch to draw similarities between the two systems, and I am not trying to do that; it is just that CMU's system was the first thing that came to my mind. Anyways, as compared to this system, CNN hologram acquisition setting appears rather miniature in terms of the complexity of both the scene and the tracking issues. I am not sure if in the Superbowl system, the vision problems were circumvented by the over-constrained camera systems, but (to me) that definitely appears to be the case in the hologram system.

One technology that I would like to see in near future (and I believe the required positive research results exist) is better processing of audio signal, mostly when SNR is very low. In particular, when the journalists were reporting amidst the crowd, even though they were shouting at the top of their lungs, the signal was inaudible. I think, with the state-of-the-art audio processing techniques, this problem should be manageable.

I think what I wish to see in future is a clearer audio signal and less constrained settings for the hologram acquisition. In other words, I wish to see the person "beamed" directly from outdoors (as opposed to from inside a customized trailer), which I suppose will open a can of "vision-related" worms (problems).

That said, I am very excited to see the fast transition of the research in vision, signal processing, and human computer interface into mainstream / popular technology. No matter how hard the cynics lambaste/ridicule these demonstrations, there is no doubt that these things are inspiring a lot of young and creative minds.