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researcher

Ruiyi Zhang

Adobe Research

19 papers hereh-index 203.4k citations37 works total

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

author position
  • first author5
  • middle author11
  • last author1

Across the 17 of 19 papers where every author was matched, so the position is known.

fields
  • stat.ML7
  • cs.CL6
  • cs.CV3
  • cs.LG2
  • cs.AR1
affiliations
  • Adobe Research
Homepage
same name
  • Ruiyi Zhang — 27 papers, h 9
  • Ruiyi Zhang — 21 papers, h 10
  • Ruiyi Zhang — 18 papers, h 12
  • Ruiyi Zhang — 12 papers, h 6
  • Ruiyi Zhang — 10 papers, h 6
  • Ruiyi Zhang — 7 papers, h 10

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
20172023
most citedAdversarial Text Generation via Feature-Mover's Distance

73 citations · 519 across the 19 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CV2019★ 7 cited

Figure Captioning with Reasoning and Sequence-Level Training

Charles Chen, Ruiyi Zhang, Eunyee Koh +5

Figures, such as bar charts, pie charts, and line plots, are widely used to convey important information in a concise format. They are usually human-friendly but difficult for comp…

cs.CL2019★ 56 cited

Topic-Guided Variational Autoencoders for Text Generation

Wenlin Wang, Zhe Gan, Hongteng Xu +5

We propose a topic-guided variational autoencoder (TGVAE) model for text generation. Distinct from existing variational autoencoder (VAE) based approaches, which assume a simple Ga…

stat.ML2019★ 8 cited

Scalable Thompson Sampling via Optimal Transport

Ruiyi Zhang, Zheng Wen, Changyou Chen +1

Thompson sampling (TS) is a class of algorithms for sequential decision-making, which requires maintaining a posterior distribution over a model. However, calculating exact posteri…

cs.CL2019★ 23 cited

Improving Sequence-to-Sequence Learning via Optimal Transport

Liqun Chen, Yizhe Zhang, Ruiyi Zhang +7

Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word…

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