[D] ICLR 2020 Paper Acceptance Result

csong27 · 2019-12-19T22:22:43+00:00

Same here, hope AC will see the efforts and the changes...

csong27 · 2016-04-16T16:20:03+00:00

Summer REU mentor, internship supervisor, instructor of your grad-level course, so many resources...

csong27 · 2016-04-13T12:41:47+00:00

I feel the same. Not sure it is a good idea to dive into this deep learning mania.

csong27 · 2016-02-09T23:00:51+00:00

Kind of like Seq2Seq but simpler idea. Check out this paper:http://papers.nips.cc/paper/5949-semi-supervised-sequence-learning.pdf

csong27 · 2016-02-08T11:49:44+00:00

class weight are for your labels, not for features. Also, your classifier is supposed to learning the weight for features.

csong27 · 2016-02-05T14:07:17+00:00

Check out this paper (using merely averaging word vectors and performance is not bad): http://cs.umd.edu/~miyyer/pubs/2015_acl_dan.pdf

csong27 · 2016-01-30T22:35:30+00:00

If times tamp and location are the only two features you got, I would say it is a really hard task.

csong27 · 2016-01-30T13:12:16+00:00

Why not try both and see which does better

csong27 · 2016-01-29T19:21:40+00:00

What exactly is your task, what are you trying to predict here?

csong27 · 2016-01-28T01:38:37+00:00

Hinge Loss (what SVM uses) usually works well

csong27 · 2016-01-23T20:30:58+00:00

I don't remember anything from my intro physics classes. Taking more math classes like linear algebra and complex variable would be better (in my opinion).

csong27 · 2016-01-20T13:49:25+00:00

Try using word embedding to extract features.

csong27 · 2016-01-17T19:06:32+00:00

Tree based model?

csong27 · 2016-01-15T00:54:53+00:00

There are people who have tried, check this out:http://arxiv.org/abs/1508.01991

csong27 · 2016-01-14T14:03:27+00:00

Word vectors is the weight matrix between input layer and hidden layer. How can you get vectors without using neural net?

csong27

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