On MNIST, learned block-rotation 'monoidal' embeddings outperform fixed truncated DFT embeddings, with the gap growing as embedding dimension drops to 2.
Gradient- based learning applied to document recognition
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
Directional Non-Commutative Monoidal Embeddings for MNIST
On MNIST, learned block-rotation 'monoidal' embeddings outperform fixed truncated DFT embeddings, with the gap growing as embedding dimension drops to 2.