A list of commonly asked interview question for university lectureship and fellowship positions
Three most important lessons about neural networks and word embeddings: 1. No free lunch, 2. Size matters, 3. Engineering matters
About two months ago, a paper that resulted from my Ph.D. work has been published in the International Journal of Document Analysis and Recognition. The paper is titled “A framework for information extraction from tables in biomedical literature”.
For a while, I have been preparing for this step. This blog got it’s audio podcast show and it is called Inspiratron talks. First I will give some links and then talk about mission and vision of the podcast. Inspiratron talks is hosted at the moment on SoundCloud:
At the moment, two episodes can be found. However, I am hoping to release one episode per week during the season, however, there may be some breaks around the Christmas and during the summer.
You can listen to podcasts using the following Podcast directories:
www.stitcher.com/podcast/inspirat…talks?refid=stpr …
www.acast.com/inspiratrontalks
www.blubrry.com/inspiratron/
This is the story how one schema I worked on as a side project suddenly found its place in W3C recommendation.
In November 2015, I went with my supervisor to Japan. In small cities of Mishima and Ito, about 1 hour train ride from Tokyo was held Biomedical Linked Annotation Hackathon (BLAH2) to which my supervisor was invited. He could not stay for the whole period, so he offered me to go, which I accepted. The event was organised by Japanese Database Center for Life Sciences (DBCLS).
On the first day was the conference, where people were presenting their work mainly on annotating biomedical literature. My PhD was related, kind of similar topic, it was about information extraction from tables
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With 2 friends of mine (Team was: Maksim Belousov, Mike Phuycharoen, Nikola Milosevic (myself)) on 12th and 13th November I participated at the GreatUniHack that was held in John Dalton building of Manchester Metropolitan University. We were implementing idea of machine learning-based music composer, for which we later came with name SchumaNN. For some time I wanted to experiment with this idea and this 24 hour hackathon came as a perfect match. We had couple of meetings prior to hackathon when we discussed some solutions and papers/blog posts we read. Some of the approaches we found are:
The decision we went at the end is to create a recurrent
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Usually I don’t write about services I use, but one came into my way that I like – Quantopian. For a while I am interested in financial side of computer science, quantitative analysis, machine learning applications to predicting a stock market, etc. However, I was not really able to get grasp of these fields. I certainly could get some data from either Yahoo Finance and I even had access to Bloomberg terminal to play a bit with at the University library. I made some models, published a paper or ArXiv , made a tool that make these models available and published it on GitHub and I am talking with people at MUTIS Finance society to make a Quant
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Recent events shocked the World. However, I don’t really want to talk about high casualties in Nice or Turkey in recent two days, event thought this post was motivated by these events. We live in a world that is shaking unstable. There are terrorist attacks almost on monthly bases, many Middle East and African countries are in civil wars, we see a lot of racism and religious non tolerance, etc. However, if you look at the bigger picture and what is causing these conflicts, they are irrelevant and even stupid. I would be brave enough to say that most of the conflicts people have (between people or between nations, groups, etc.) are usually motivated by stubbornness of a crazy little
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For a while I was playing with financial data and financial data mining. I have already written about it in a separate post. Now, I decided to try to make my research so far accessible a bit more, so people can use it. Basically, what I previously did is that I used a bunch of technical financial parameters, such as price, P/E ratio, Price to Sales, Price to Book and many other ratios to try to create a model that will be able to predict a stock price movement over the long term (1 year period). It seems that it is working for about 75% of times using machine learning, namely Random Forests algorithm. For more details you can
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