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Showing posts from July, 2015

Canterbury Tales Neural Network

I trained another RNN (multi-layer recurrent neural network), this time to generate poems in Middle English in the style of Canterbury Tales. It started generating interesting stuff after just a few minutes of training, but I let it finish anyway. I noticed in one of the samples that it had generated a title, so I seeded it with that title, and sure enough, it closed the poem eventually and started a new one. Kinda cool. The numbers are line numbers (you can see they're obviously not accurate), and it generates footnotes, too, since they were in the source text. The indentation and spacing is all generated by the neural net, too.

  This walmeth have,' quod Melibee, 'by see                   760   For it was grave, and of my voys I may,   To hir, with-outen fond to wedden she                         285   How that a man unto pituk than;                              1160   Til that were I his messaille aboghnis.   For, in the sovereyntes ful marchant   To me, night ·…

A neural network that writes Scalia dissents

I trained a recursive neural network ( on a bunch of Justice Scalia's dissents from the past few years. It spits out some amusing stuff, depending on the starter text and how "adventurous" you want the output. Since it's character-based and not word-based, it makes a bunch of spelling errors (unlike Justice Scalia), but is also able to create new words (just like Justice Scalia!). Here are some samples.
Starter text: "Justice SCALIA", random level: 0.8. Never would have expected this from a strict constructionist (check the first sentence). This one brings in same-sex marriage, constitutional interpretation, and the typical contempt-ridden air quotes.

"Justice SCALIA, dissenting. The Constitution is an opinion, and so views that "[t]he Court tait the structure relations (interneline) rejectly and weands is not categorical, while all this one inference to do be not applying a nample between the fairest regul…