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researcher

Ge Wang

81 papers hereh-index 7024.6k citations658 works total

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

author position
  • sole author1
  • first author4
  • middle author22
  • last author48

Across the 75 of 81 papers where every author was matched, so the position is known.

fields
  • physics.med-ph31
  • eess.IV15
  • cs.CV14
  • cs.LG9
  • q-bio.QM4
  • physics.bio-ph3
same name
  • Ge Wang — 15 papers, h 10
  • Ge Wang — 14 papers, h 12
  • Ge Wang — 13 papers, h 8
  • Ge Wang — 12 papers, h 4
  • Ge Wang — 9 papers, h 4
  • Ge Wang — 9 papers, h 3

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
20102021
most citedLow-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

1.9k citations · 6.7k across the 80 of their papers we have counts for

collaborators
Showing 2018 · cs.LGShow all

4 papers · 2 filters

cs.LG2018★ 1 cited

Soft Autoencoder and Its Wavelet Adaptation Interpretation

Fenglei Fan, Mengzhou Li, Yueyang Teng +1

Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpr…

cs.LG2018★ 3 cited

On a Sparse Shortcut Topology of Artificial Neural Networks

Fenglei Fan, Dayang Wang, Hengtao Guo +4

In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effe…

cs.LG2018★ 6 cited

Universal Approximation with Quadratic Deep Networks

Fenglei Fan, Jinjun Xiong, Ge Wang

Recently, deep learning has achieved huge successes in many important applications. In our previous studies, we proposed quadratic/second-order neurons and deep quadratic neural ne…

cs.LG2018

Shamap: Shape-based Manifold Learning

Fenglei Fan, Ziyu Su, Yueyang Teng +1

For manifold learning, it is assumed that high-dimensional sample/data points are embedded on a low-dimensional manifold. Usually, distances among samples are computed to capture a…

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