Gzip does not support a "semantic" mode, hence it won't and does not (according to the papers metric) perform as well.

Deep learning can capture these semantic similarities.

Yahoo Questions it is not top performer. It's not far fetched to think that news are written in a similar way, sometimes even partly copied, and therefore have a lot of words in common. Yahoo Questions is a forum and I'd expect there to be a greater variation of word, but the word themself have a semantic similarity.

That is, gzip is strong when many words overlap (the size increase when gzipped is smaller) but if it's semantic similarity DNN's win everyday.

The results are interesting but not as interesting as it sounds IMO.

Mainly Data Science or Kotlin I'm blogging about it at least for now.

Comments URL: https://news.ycombinator.com/item?id=36550924

Points: 2

# Comments: 0

We solved 1. the same way, but it felt "off" somehow. Perhaps it's a good solution.

2. That's a sound solution, but a tiny bit cumbersome. I have projects where we deploy both classifier and regressor, where it'd nice to keep all in main. Alas, you can't have it all.

1. Harder to track experiments on remote VM's (e.g. Azure) as there's no server (we need to feed results back somehow) 2. Impossible (?) to track different types of experiments in the same repo. MLFlow has a way to define experiments and runs, which means I can easily group Regression vs Classification or even if I try a completely different task with the same data.

If anyone has a good suggestion on how to solve these two I'd love to fully commit to DVC!

Really smooth, love it!

What would you say differentiates you from Smile which includes a simplistic datagrame, visualisation and support for CBLAS etc.

Is speed on par?

There's another solution named LIME which seems to take a similar but more general approach, I like this more tailored idea as it'll probably give a better interpretation of the NLP questions.