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researcher

S. Dubey

57 papers hereh-index 407.6k citations132 works total

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

author position
  • sole author3
  • first author11
  • middle author28
  • last author14

Across the 56 of 57 papers where every author was matched, so the position is known.

fields
  • cs.CV46
  • cs.LG5
  • eess.IV5
  • eess.SP1
same name
  • S. Dubey — 134 papers, h 21
  • S. Dubey — 25 papers
  • S. Dubey — 6 papers, h 3
  • S. Dubey — 5 papers, h 2
  • S. Dubey — 5 papers, h 2
  • S. Dubey — 4 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
20142026
most citedHybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification

1.9k citations · 3.4k across the 51 of their papers we have counts for

collaborators
Showing 2021 · cs.LGShow all

3 papers · 2 filters

cs.LG2021★ 18 cited

Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Shiv Ram Dubey, Satish Kumar Singh, Bidyut Baran Chaudhuri

Neural networks have shown tremendous growth in recent years to solve numerous problems. Various types of neural networks have been introduced to deal with different types of probl…

cs.LG2021★ 1 cited

AdaInject: Injection Based Adaptive Gradient Descent Optimizers for Convolutional Neural Networks

Shiv Ram Dubey, S. H. Shabbeer Basha, Satish Kumar Singh +1

The convolutional neural networks (CNNs) are generally trained using stochastic gradient descent (SGD) based optimization techniques. The existing SGD optimizers generally suffer w…

cs.LG2021★ 21 cited

AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks

S. K. Roy, M. E. Paoletti, J. M. Haut +4

Convolutional neural networks (CNNs) are trained using stochastic gradient descent (SGD)-based optimizers. Recently, the adaptive moment estimation (Adam) optimizer has become very…

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