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researcher

Hideki Nakayama

The University of Tokyo

43 papers hereh-index 303.5k citations123 works total

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

author position
  • first author1
  • middle author12
  • last author28

Across the 41 of 43 papers where every author was matched, so the position is known.

fields
  • cs.CV25
  • cs.CL13
  • eess.IV2
  • cs.AI1
  • physics.chem-ph1
  • q-fin.ST1
affiliations
  • The University of Tokyo
Homepage
same name
  • Hideki Nakayama — 8 papers, h 4
  • Hideki Nakayama — 6 papers, h 2
  • Hideki Nakayama — 4 papers, h 2
  • Hideki Nakayama — 3 papers, h 3
  • Hideki Nakayama — 2 papers, h 0
  • Hideki Nakayama — 1 paper, h 2

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
20162023
most citedCompressing Word Embeddings via Deep Compositional Code Learning

29 citations · 123 across the 37 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.CL2018

Real-time Neural-based Input Method

Jiali Yao, Raphael Shu, Xinjian Li +2

The input method is an essential service on every mobile and desktop devices that provides text suggestions. It converts sequential keyboard inputs to the characters in its target…

cs.CV2018★ 6 cited

Semantic Aware Attention Based Deep Object Co-segmentation

Hong Chen, Yifei Huang, Hideki Nakayama

Object co-segmentation is the task of segmenting the same objects from multiple images. In this paper, we propose the Attention Based Object Co-Segmentation for object co-segmentat…

cs.CL2018★ 2 cited

Discrete Structural Planning for Neural Machine Translation

Raphael Shu, Hideki Nakayama

Structural planning is important for producing long sentences, which is a missing part in current language generation models. In this work, we add a planning phase in neural machin…

q-fin.ST2018

Deep Learning for Forecasting Stock Returns in the Cross-Section

Masaya Abe, Hideki Nakayama

Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achiev…

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