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by hardmaru on 2018-12-08 (UTC).

Our work on “Deep Learning for Classical Japanese Literature” is out!

We introduce Kuzushiji-MNIST, a drop-in replacement for MNIST, plus 2 other datasets. In this work, we also try more interesting tasks like domain transfer from old Kanji to new Kanji.https://t.co/qmizR9KF1O pic.twitter.com/ELdDDUmEE2

— hardmaru (@hardmaru) December 8, 2018
research
by hardmaru on 2018-12-08 (UTC).

This work will be presented at the #NeurIPS2018 Workshop on Machine Learning for Creativity and Design by @tkasasagi, in collaboration with @mikb0b, @kitamotoasanobu, Alex Lamb, Kazuaki Yamamoto, and myself. Here are more details about the dataset:
https://t.co/RJKPZvh5ZA

— hardmaru (@hardmaru) December 8, 2018
research
by hardmaru on 2018-12-08 (UTC).

Summary of the paper: https://t.co/dZlIAyYIC0

— hardmaru (@hardmaru) December 8, 2018
research
by mikb0b on 2018-12-09 (UTC).

A t-SNE projection of Kuzushiji-MNIST.

You can see how several classes have a multi-modal distribution in this space - this is because the characters have several distinct ways of being written.

Plot by @_sw1227_
from https://t.co/Iyu7OSpEvM (jp) pic.twitter.com/6M7XWRbUz6

— Mikel Bober-Irizar (@mikb0b) December 9, 2018
datasetdataviz
by hardmaru on 2018-12-11 (UTC).

Zooming in allows one to get a glimpse of the world before our time. pic.twitter.com/9Z3rMbwyLD

— hardmaru (@hardmaru) December 11, 2018
researchdatasetdataviz

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