The importance of socializing

Socializing is an odd term. People in Computer Science are usually regarded as socially incompetent, or at least plain out weird and introverted. Funny, though, that most of the modern tools (Twitter, Facebook, Google+) people use to interact were created by these socially impaired persons.
But, then again, people from CS are usually the ones that post more actively and have many friends in Facebook and Twitter. So, they do socialize.....

Yet, we are not here to point out this oddities, but to speak on the importance of socializing (which is different from team work) in a research environment. Also, I'll address and recommend some particular sites fro the ML community.

I've found that I feel motivated when I discuss different topics with different people. And even if the problem is different, you still feel good. How many times have you discussed with your adviser or a colleague and found out something you did not consider. Sometimes our thoughts are different when we externalize them. A good idea may seem dumb when spoken out-loud, or a bad idea may have a touch of genius when others hear it.

Teaching something you like is another way to socialize. Prepare a presentation on some random topic, and you'll see you can learn a lot from that topic (if presented to the right audience). I highly endorse making the student present their work or a random topic to an audience. It forces them to understand the material, and in the process they learn a bit on expressing their ideas.

But in this modern age, we have a myriad of tools to achieve these interactions without getting out of our desks (which I have yet to decide whether is a good or a bad thing). We have social networks, where we can find good researchers, we have online lectures, were we can socialize with other people watching the lecture, and thankfully machine learning has been an early adopter as well as an active player.

On social networks, you can have different interactions depending the network you use. I find Twitter lists to be a great source of Machine Learning researchers, feel free to follow mine. Google+ recently releases a feature that allows the users to share their circles, and I have a somewhat good circle of Machine Learning Researchers (Andrew Ng, Nir Friedman and Yan LeCun among others). The important thing about these lists and circles are not only the researchers, but the enthusiastic community of grad students that post, discuss and share ideas and innovations.

Now, I've found that while great, these networks are not really suited for a deep theoretical discussion, and with the advent of QA sites like Quora, I think we have a better forum to externalize doubts and consultations on ML and other topics.

I think the top QA venue in Machine Learning is Metaoptimize (lets plug the ad here). It is a place, where most of the people are devoted Machine Learning students (unlike Reddit's Machine Learning sub-reddit, where most of the people are ML enthusiasts).

In Metaoptimize, you usually will get good answers for most of your questions, and if you don't, you get at least a link and a starting point to keep looking for an answer. People there have their fundamentals right, so if you have specific questions on the intuition and maths of a problem, you'll get your answer there. Most of the top contributors in Metaoptimize are also in my Twitter and Google+ lists.

Metaoptimize is definetily a step in the right direction, but sill, I think more can be done to share and to create a broader community, Andrew Ng and Norvig have recently opened free courses to the world, in which they teach ML and AI. They haven't started yet, but a hefty amount of students are rallying to them.

Our world is open, do not think that doing research in a desk involves only reading books and papers, socializing is a very important part of it, and one you should embrace.

That's everything for tonight


Remember to visit my webpage www.leonpalafox.com. And if you want to keep up with my most recent research, you can tweet me at @leonpalafox.
Also, check my Google+ account, be sure to contact me, and send me a message so I follow you as well.

Every time you write a bad paper an angel cries

"Writing is an art, every word has a specific meaning, and each expressions should hold an objective."

Scientific writing, unlike any other kind of writing has a very specific goal: To report your findings in a clear and concise way. Your opinion while important, has to leave most of the space for the cold, hard, facts. I read once that in writing, academics are like civil engineers, their work is to make sturdy structures that will have a solid foundation, leave the niceties and decorations to the architects.

But how do you know what to write? Usually a good paper's results can be replicated by an informed reader. (In the NIPS reviewers poll, that is one of the conditions). Thus, your paper needs enough information so a fellow researcher does not need to ask you any questions.

Sound easy right? Truth is, it isn't.

Sadly, most people thing that because they can speak, they can write. A large number of people, think that writing the way they speak is the way to do a research paper (or any online publication). However, clarity is a luxury we usually forget when we speak, for example, while saying that there was "a lot" of people in the market is a great expression when telling a story, in a research paper saying that they had "a lot" of data is non representative of the amount, and thus ambiguous.

A research paper, unlike speaking, allows us to rewrite our sentences several times. Remember that is impossible to ask for clarification when reading an article (unless you send an email), so you have to write as clear as possible. All those times you've had to clarify what you meant with a sentence when speaking, are dead sentences in writing. My rule of thumb is that a reader has to  understand the paper without me sitting next to them.

But how do we learn to write well? If you are lucky, your advisor is still very prolific, and a lifetime of reading and writing papers have given him at least a good idea of how to structure a paper. With time, you too will have a hold of the most common rules of writing a research paper. Something I tend to do is check for verb consistency, is every noun's action being described by a verb, or not. If you have free nouns in your paper, you will have confusion. If you're not sure, keep yourself from writing complex statements, and instead hold to the basics.

There are other tools you can use to improve your writing. "The elements of Style" by Strunk and White and "On Writing Well" by Zinsser are 2 great books which offer great and basic advice on how to structure a good piece. While the books are mostly oriented towards people writing real literature (for me academic journals are more like technical reviews), they do help you to structure your sentences so they become less ambiguous (clarity, clarity, clarity).

My advise is to practice, you can try writing a blog, writing papers for small conferences, or even mock papers (there is no rule against them) and look for your mistakes, look for things you could write in a better way. Try asking people to read your documents and see how much sense it made to them. Really, I think you have to  keep practicing over and over, until you get a firm grasp on paper writing.

Another really important thing is to review. If your paper has typos, it speaks ill of you and your research, it shows you as a sloppy author and thus a sloppy researcher. Take your time to re read your papers. I usually spend around 3 days writing a blog post, first I write it in what we could barely call English, then I re-read the entry and I try to give sense by taking out sentences, useless words and adding clarity. Then I use tools like Microsoft spell checking and style checking to look for the use of odd sentences and passive voice (extreme use of passive voice in a paper adds confusion and usually is better to write using an active form)

It is fair to say I spend 2 to 3 times more doing the reviewing than the writing of the piece, but I know it helps me to write better the next time. So I don't look at it as a burden, but rather as practice.

If you have any other advise on writing let the comment section hear them.


Remember to visit my webpage www.leonpalafox.com. And if you want to keep up with my most recent research, you can tweet me at @leonpalafox.
Also, check my Google+ account, be sure to contact me, and send me a message so I follow you as well.

Starting to write

What is the best way to start a book?

Many people will tell you that the best way is to gather experience and then write your book. Others will tell you to write as you gather experience. Truth is..... I do not know yet who is right. While it is true that a lot of great books are written after experience has been learn, it is also true that a lot of great books are written on the go.

So how does this translates to papers in Machine Learning?

For most graduate programs, you'll need to write Conference and Journal papers. In some universities, though, they are not really picky on which journals or conferences, as long as it's published. Others, do care to which conference do you go. Not to mention that your Professor probably has already a set of conferences where he is a regular, thus, asking you to write a paper for such conferences.

So, back to the basic question, do you write as you go, or do you write once you finished every experiment, theory and survey? (at the end of your PhD). Some people will tell you the former is the better, while others, will tell you that is good to have a ton of papers, since most committees will rarely look at the papers themselves, but just at the sheer number of publications.

I think a fair balance is the best policy, you do have to wait until you have good results to publish, but you also have to publish enough so you get to go to different conferences and get feedback from experts in the area you are working in, or at least a good networking. Be careful to remember that really good journal or conferences won't accept papers of half-done research or quick baked results.

Remember, though, that most journals will take their sweet long time to accept your paper, or even reject it. So, if you wait until the end to submit it, you'll definitively will have troubles getting your paper accepted by the end of your PhD. (We are speaking that some IEEE journals take around 7 to 8 months to give you any feedback)

Lastly and most important, when in Rome, do as Romans do. Regardless on your opinion, truth is, you are a simple student. Thus, if you prefer to wait, and your committee wants you to publish.....you'll either publish or go. On the other hand, if the University wants you to go to specific conferences (acceptance rates around 20 or 30 %), you will have to wait, even if you badly want to publish any incremental gain you''ve had. 

Good luck writing your first paper, and in the next post, we'll talk about on how to write, and why it is very important for you to be extra careful when writing a paper.

Remember to visit my webpage www.leonpalafox.com. And if you want to keep up with my most recent research, you can tweet me at @leonpalafox.
Also, I've recently started using more and more my Google+ account, be sure to contact me, and send me a message so I follow you as well.