One thing you don&#x27;t mention in your paper reading method is a stopping criterion. I&#x27;m opening and doing some level of &quot;looking at&quot; --- not necessarily reading! --- probably 10-20 papers a week. More if you count revisits to papers that I&#x27;ve &quot;looked at&quot; before.<p>The question is, how do you decide which papers to promote to further attention? It&#x27;s always a ranking problem, because there&#x27;s always other work you could be looking at. You start your reading method at the point at which you&#x27;ve already decided to read the paper. But how do you get to that point?<p>(I can describe how I decide, but it&#x27;s not exactly easy to replicate. It&#x27;s a pretty insidery perspective. Here&#x27;s how it played out for this paper.<p>I looked at this paper soon after it was published because I follow the first author on Twitter. When I opened the paper I looked at who was on it. I know Phil, and I know the sort of work Ed has been doing. I glanced over the paper and looked at the experiments, and saw that it was similar to the neural turing machine and algorithm learning line of work, that&#x27;s still being evaluated on toy problems. This work is interesting but I&#x27;m not doing research on these things, so I won&#x27;t commit to learning it while it&#x27;s moved &lt;1 mountain empirically. I did read the idea enough to contrast it with Chris Dyer&#x27;s stack LSTM parser, which is a model that performs very well empirically. I checked the related work for criticism&#x2F;comment on that work, and the paper said it&#x27;s inspired by it. Cool. I&#x27;ll watch for future empirical evaluations.<p>In total I probably spent about 10-15 minutes looking at this paper. This is enough for me to remember I&#x27;d seen the work, and help me understand future related ideas a little bit better.)