What’s Hot In Your Field

From Apple Special Event. September 12, 2017.

Finding an interesting problem to solve is quite important in research. It is also important that you know what other researchers find interesting. Mainly to understand where your field is moving. But there are several other practical reasons why it is important to know what’s hot in your field. For example you will have more opportunity to collaborate with other researchers, more datasets and code will be released and more challenges and workshops will be organised and more funding will be available for hotter problems.

Although arXiv has become a venue itself for Computer Science, the signal to noise ratio is still quite low. So IMHO, conferences are the best places to look for quality work. But with hundreds of papers getting accepted, it is not very easy to go over the whole paper list for even one conference. Considering there might be more than one conference you follow, even a simple task such as browsing the interesting papers might take plenty of time. So I’ve decided to write a script to do that for me.

I have been using it for some time and decided to share it. It’s not perfect, or there is nothing fancy going on, but it does the job for me. Any way here’s the code.

Now let’s see how it works. NIPS 2017 accepted papers are recently announced. Here’s the output for NIPS 2017:

There are a lot of neural networks, right :D That’s one way to look at it, but variational inference and gaussian processes are getting more popular, I guess.

This time let’s look at what EMNLP 2017 has to say. Here’s the output:

This time a lot of machine translation, right :)

Topics to watch are relation extraction, semantic parsing, question answering and cross lingual.

You might notice that there are 344 papers accepted, my n-gram match script filtered the whole list for me down to 25, which is somewhat more manageable. Also EMNLP people seems to prefer longer titles :D

Finally, let’s look at what CVPR 2017 has to say:

This time, as one might expect we have a lot of convolutional. But we also have plenty of weakly supervised, pose estimation, zero shot, person identification and finally question answering.

Conclusion

I thought I could do a word embedding based retrieval instead of simple a word match to find the relevant papers but then I wanted to keep it simple and didn’t want to postpone this post any more.

You can find the code and the sample files here. Hope it helps.

PhD student at Hacettepe and Team Lead at STM doing Deep Learning in Vision and Language.