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Zhengyang Wang

Texas A&M University

5 papers hereh-index 161.8k citations24 works total

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

author position
  • middle author4

Across the 4 of 5 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.CV1
  • eess.IV1
  • math.RT1
affiliations
  • Texas A&M University
same name
  • Zhengyang Wang — 10 papers, h 12
  • Zhengyang Wang — 6 papers
  • Zhengyang Wang — 1 paper, h 2
  • Zhengyang Wang — 1 paper

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
20172019
collaborators

5 papers

eess.IV2019

Global Pixel Transformers for Virtual Staining of Microscopy Images

Yi Liu, Hao Yuan, Zhengyang Wang +1

Visualizing the details of different cellular structures is of great importance to elucidate cellular functions. However, it is challenging to obtain high quality images of differe…

cs.CV2018

ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions

Hongyang Gao, Zhengyang Wang, Shuiwang Ji

Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in emplo…

cs.LG2018

Large-Scale Learnable Graph Convolutional Networks

Hongyang Gao, Zhengyang Wang, Shuiwang Ji

Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs.…

math.RT2018

Supersingular representations of rank 1 groups

Karol Koziol

We prove that any connected reductive group of semisimple F-rank 1 over a p-adic field admits an irreducible admissible supersingular mod-p representation. This establishes o…

cs.LG2017

Pixel Deconvolutional Networks

Hongyang Gao, Hao Yuan, Zhengyang Wang +1

Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models fo…

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