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S. Amari

26 papers hereh-index 9647.3k citations579 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author8
  • middle author5
  • last author11

Across the 26 of 26 papers where every author was matched, so the position is known.

fields
  • stat.ML5
  • math.ST4
  • q-bio.NC4
  • cs.IT3
  • cond-mat.dis-nn2
  • cs.LG2
same name
  • S. Amari — 12 papers, h 51
  • S. Amari — 2 papers, h 2
  • S. Amari — 1 paper, h 3
  • S. Amari — 1 paper
  • S. Amari — 1 paper, h 30

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20102023
most citedBayesian Robust Tensor Factorization for Incomplete Multiway Data

247 citations · 967 across the 25 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

math.ST2020

Wasserstein Statistics in One-dimensional Location-Scale Model

Shun-ichi Amari, Takeru Matsuda

Wasserstein geometry and information geometry are two important structures to be introduced in a manifold of probability distributions. Wasserstein geometry is defined by using the…

stat.ML2020★ 11 cited

When Does Preconditioning Help or Hurt Generalization?

Shun-ichi Amari, Jimmy Ba, Roger Grosse +5

While second order optimizers such as natural gradient descent (NGD) often speed up optimization, their effect on generalization has been called into question. This work presents a…

math.ST2020

Wasserstein statistics in 1D location-scale model

Shun-ichi Amari

Wasserstein geometry and information geometry are two important structures introduced in a manifold of probability distributions. The former is defined by using the transportation…

stat.ML2020★ 3 cited

Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective

Shun-ichi Amari

It is known that any target function is realized in a sufficiently small neighborhood of any randomly connected deep network, provided the width (the number of neurons in a layer)…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.