The authors identify a Golden Partition Zone based on an intra-class variance shift in entropy bounds that enables intrinsic model inversion resistance when partitioning neural networks for collaborative inference.
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Deep Learning for Classical Japanese Literature
23 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the perspective of ML researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. In this work, we introduce Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as well as two larger, more challenging datasets, Kuzushiji-49 and Kuzushiji-Kanji. Through these datasets, we wish to engage the machine learning community into the world of classical Japanese literature. Dataset available at https://github.com/rois-codh/kmnist
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Stimulus symmetries can confound representational similarity analyses
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