What’s Hot In Your Field

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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 680 accepted papers
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neural networks 34
reinforcement learning 24
deep learning 12
variational inference 11
deep neural networks 11
deep neural 11
generative adversarial 10
gaussian processes 10
gradient descent 9
neural network 9
large scale 8
deep reinforcement 7
graphical models 7
deep reinforcement learning 7
coordinate descent 7
online learning 7
recurrent neural 7
adversarial networks 6
semi supervised 6
multi agent 6

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:

There are 344 accepted papers
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machine translation 29
neural machine 23
neural machine translation 23
neural networks 11
reinforcement learning 9
relation extraction 9
word embeddings 8
semantic parsing 7
based neural 7
question answering 7
sentiment analysis 6
fine grained 6
dependency parsing 5
cross lingual 5
attention based 5
natural language 5
entity recognition 4
language models 4
sequence models 4
sense disambiguation 4

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:

There are 784 accepted papers
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neural networks 31
convolutional neural 24
weakly supervised 19
action recognition 17
semantic segmentation 16
pose estimation 16
convolutional neural networks 15
object detection 15
neural network 15
deep learning 14
zero shot 13
person identification 12
deep neural 11
spatio temporal 11
question answering 10
representation learning 10
convolutional networks 9
shot learning 9
optical flow 9
image classification 9

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.

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