◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

R. Sato

27 papers hereh-index 14950 citations29 works total

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

author position
  • sole author10
  • first author10
  • middle author7

Across the 27 of 27 papers where every author was matched, so the position is known.

fields
  • cs.LG14
  • cs.IR5
  • cs.AI2
  • cs.CL2
  • cs.DL2
  • stat.ML2
same name
  • R. Sato — 105 papers, h 52
  • R. Sato — 55 papers, h 26
  • R. Sato — 44 papers, h 27
  • R. Sato — 11 papers
  • R. Sato — 7 papers, h 10
  • R. Sato — 5 papers

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
20192026
most citedA Survey on The Expressive Power of Graph Neural Networks

83 citations · 160 across the 26 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023★ 4 cited

Embarrassingly Simple Text Watermarks

Ryoma Sato, Yuki Takezawa, Han Bao +2

We propose Easymark, a family of embarrassingly simple yet effective watermarks. Text watermarking is becoming increasingly important with the advent of Large Language Models (LLM)…

cs.CL2023★ 2 cited

Necessary and Sufficient Watermark for Large Language Models

Yuki Takezawa, Ryoma Sato, Han Bao +2

In recent years, large language models (LLMs) have achieved remarkable performances in various NLP tasks. They can generate texts that are indistinguishable from those written by h…

cs.LG2023★ 2 cited

Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence

Yuki Takezawa, Ryoma Sato, Han Bao +2

Decentralized learning has recently been attracting increasing attention for its applications in parallel computation and privacy preservation. Many recent studies stated that the…

cs.LG2023★ 1 cited

Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure

Ryoma Sato

Graph Neural Networks (GNNs) are popular models for graph learning problems. GNNs show strong empirical performance in many practical tasks. However, the theoretical properties hav…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.