Skip to content

Comment on Neural Symbolic Machines: Learning Semantic Parsers with Weak Supervision

Comments

I know that the field of deep learning / machine learning generally moves too fast for researchers to target conferences or journals, but several of the citations in the PDF of this article are missing (LaTeX inserted [?]).

(GRU, in case anyone is wondering, stands for "gated recurrent unit" and is a building block of standard LSTMs)

EDIT: now that I've finished the paper, I've realized that the citations are straight-up missing. That's no good, but I'm sure the authors just messed up the arxiv upload. If OP knows them, they should let them know... failing to include any citations at all is a quick way to decrease the credibility of an article.

A GRU is more like a simplification of the ideas in LSTM, rather than a building block. At a high level, it uses the hidden state as the memory of the cell (rather than a separate cell state) and it uses a single "update" gate, merging the forget and input gates. Overall it performs similarly to LSTM while being more computationally efficient (fewer matrices).

FWIW, my guess is that a lot of the novel stuff that has been released in the last week is because of the impending ICLR deadline (Friday). The review process for that conference allows the papers to be updated until the reviewers' decisions are made. So getting the text 'finalized' isn't an essential step right now.

I've let them know already. I think it is just a LaTeX compilation issue.

If you look at the source you see that the \cite{} statements do reference meaningful anchors but that the bibtext file seems to be missing from the LaTeX archive:

https://arxiv.org/format/1611.00020v1

A GRU is a simplified version of the LSTM, not a building block.

AboutSource Built by g1lg1l

Hackerly is an independent reader for Hacker News, built on the public HN API. Not affiliated with Y Combinator.