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Ghulam Rasool

13 papers hereh-index 12337 citations33 works total

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

author position
  • middle author8
  • last author5

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.LG6
  • cs.CV3
  • eess.IV2
  • eess.SP1
  • stat.ML1
same name
  • Ghulam Rasool — 14 papers, h 11
  • Ghulam Rasool — 5 papers, h 4
  • Ghulam Rasool — 2 papers, h 2
  • Ghulam Rasool — 2 papers, h 9
  • Ghulam Rasool — 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
20182025
most citedExploring Robust Architectures for Deep Artificial Neural Networks

17 citations · 59 across the 10 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023

Variational Density Propagation Continual Learning

Christopher Angelini, Nidhal Bouaynaya, Ghulam Rasool

Deep Neural Networks (DNNs) deployed to the real world are regularly subject to out-of-distribution (OoD) data, various types of noise, and shifting conceptual objectives. This pap…

cs.CV2023

Deep Ensemble for Rotorcraft Attitude Prediction

Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool +3

Historically, the rotorcraft community has experienced a higher fatal accident rate than other aviation segments, including commercial and general aviation. Recent advancements in…

cs.LG2023

The Importance of Robust Features in Mitigating Catastrophic Forgetting

Hikmat Khan, Nidhal C. Bouaynaya, Ghulam Rasool

Continual learning (CL) is an approach to address catastrophic forgetting, which refers to forgetting previously learned knowledge by neural networks when trained on new tasks or d…

cs.CV2023★ 13 cited

Efficient Scopeformer: Towards Scalable and Rich Feature Extraction for Intracranial Hemorrhage Detection

Yassine Barhoumi, Nidhal C. Bouaynaya, Ghulam Rasool

The quality and richness of feature maps extracted by convolution neural networks (CNNs) and vision Transformers (ViTs) directly relate to the robust model performance. In medical…

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