154 citations · 717 across the 63 of their papers we have counts for
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cs.LG2023★ 1 cited
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?
Tokio Kajitsuka, Issei Sato
Existing analyses of the expressive capacity of Transformer models have required excessively deep layers for data memorization, leading to a discrepancy with the Transformers actua…
cs.LG2023★ 1 cited
Exploring Weight Balancing on Long-Tailed Recognition Problem
Naoya Hasegawa, Issei Sato
Recognition problems in long-tailed data, in which the sample size per class is heavily skewed, have gained importance because the distribution of the sample size per class in a da…