Ghassan Hamarneh

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 6 papers; trusts 391 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 198 citations into it; #14,043 of 21,575 by trust here, #8,409 by citations
• Artificial Intelligence — 104 citations into it; #28,833 of 35,345 by trust here, #17,412 by citations
• Computational Mechanics — 30 citations into it; #6,250 of 10,001 by trust here, #3,190 by citations
• Model Architectures — 28 citations into it
• Optimization & Training — 25 citations into it
• Computer Graphics and Computer-Aided Design — 15 citations into it
• Efficiency & Compression — 8 citations into it
• Media Technology — 8 citations into it

Whom Ghassan Hamarneh trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yann LeCun
6. Yoshua Bengio
7. Ilya Sutskever
8. Ross B. Girshick
9. Andrew Zisserman
10. Christian Szegedy

Who trusts Ghassan Hamarneh overall

1. Vladimir G. Kim
2. Yuexiang Li
3. Feng Zhou

Papers from Ghassan Hamarneh's perspective

Ordered by how strongly the researchers Ghassan Hamarneh trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• A Survey on Shape Correspondence (2011) — 720 citations
• Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation (2018) — 434 citations
• Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets (2019) — 517 citations
• Deep semantic segmentation of natural and medical images: a review (2019) — 826 citations
• BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment (2017) — 797 citations
• Deep features to classify skin lesions (2016) — 302 citations