Transfer learning and kernel based multitask learning

I have a number of papers on transfer learning that I have started looking through for one of my research projects. Transfer learning is a machine learning technique where the basic idea is to learn multiple related tasks simultaneously, rather than learn each task independently. Previous work have shown that this works better, especially for predictive performance, compared to independent, single task learning. For my problem, I am interested in using multitask learning techniques based on natural extensions of kernel based methods for single task learning, such as the Support Vector Machines (SVMs).

Go programming language

One of my friends from the compiler group sent me an email regarding Google's new programming language, Go. From the Google talk presentation it seems this language has some unique and amazing features, and most interestingly, it feels like a language where one can code like python and get power of C++. Check out this talk if you want to know more about the language.

Procrastination weeks

Procrastination has been an old friend of mine, going back a long way. We lost touch at some point in college, except occasionally crossing each others path. The last two weeks of this quarter, procrastination has become my constant new friend. It's probably time to cut loose of this friend, except the occasional visits.

Why $\textstyle l_2$ norm?

Recent progress in signal processing theory has generated a renewed interest in the effectiveness of using the $\textstyle l_1$ norm. It is interesting to note that, for example, in sparse signal recovery one can do a more accurate signal reconstruction minimizing the $\textstyle l_1$ norm.

The reason I have been thinking about $\textstyle l_1$ norm is because it apparently looks more useful for a portion of my current research. Maybe over a hundred years ago, or so, whenever mathematicians started thinking about norms $\textstyle l_2$ won, but current research trends show that $\textstyle l_1$ norm is very handy for a number of practical purposes.

Convex optimization reading group

I am now part of a reading group on convex optimization. We are going through the amazing optimization book by Professor Boyd.

Getting busy...

This is only the second week of the fall quarter, and things have already started to pick up. I was expecting that anyways, and now that it is here, might as well embrace it. Although, there is some reluctance to accepting the busyness, not really sure why because I just got out of college a few months ago, maybe because my feeling of being 'out of school', rather then 'being in school', has not subsided as much as I thought.

Compressive sensing, again

I am currently reading a paper titled 'Random projections of smooth manifolds' by Baraniuk and Wakin. From a machine learning point of view, this paper is very interesting since it deals with dimensionality reduction, a very useful technique in machine learning research. The other great aspect of this paper is its close relation to compressive sensing (cs) ideas, a topic that I have been excited about for a while.

Graduate orientation...

Graduate orientation starts from tomorrow. It is a whole day session tomorrow, from eight in the morning till six in the evening. I am looking forward to the event tomorrow, since it is the official beginning of my new life as a phd student. There are numerous other orientation events throughout the beginning of next week, till classes begin at the end of the week. That reminds me, I need to explore more class options.

Unemployment vs PhD

This is from phdcomics, http://www.phdcomics.com/comics/archive.php?comicid=1215.


Makes one wonder whether it is worth getting a PhD or stay unemployed.

Computational tools for cooking

You don't need to be surprised reading the title, it does put Computational tools and cooking together in the same sentence. One of the perks of being a new graduate student is cooking for oneself, or rather, as in my case, learning to cook for oneself. It aptly fits in with my area of interest, machine learning, since I am learning to cook, identifying patterns in cooking from my training experience, and using those ideas to further refine my cooking to perfection.

However, I am not referring to myself by the term Computational tools in the title. Besides all the traditional tools necessary for cooking, my list involves a laptop, webcam, and internet. Honestly, I don't have a good idea of the quantity of spices to use or how to cook certain food items yet, so I take help of Google to find out recipes and figure out how much of each spices to use, if any. It is not as simple as searching for a recipe though; most times I don't have all the necessary ingredients mentioned in the recipes that I find, so I improvise by abstracting information by combining multiple recipes and some experimentation. This technique has served me adequately so far.

In the extreme case, which, very fortunately, has not happened yet, I have no idea of how to go about cooking something, skype and video chat seems like a great resource that will come in handy. Just call up someone who knows how to cook, and use the cam to show what disaster you have been up to and follow directions from that person. One great advantage of this telecooking technique is that you are not limited by geography, and can potentially go international; except that time zone might be a contributing factor.