Eyal Klang

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 7 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it; #13,085 of 21,575 by trust here, #5,333 by citations
• Artificial Intelligence — 49 citations into it; #21,052 of 35,345 by trust here, #7,172 by citations

Whom Eyal Klang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alec Radford
6. Yoshua Bengio
7. L. V. D. van der Maaten
8. Jimmy Ba
9. Yann LeCun

Who trusts Eyal Klang overall

1. Arvid Lundervold
2. Miaoyun Zhao
3. D. Malerba
4. Fabio Tanaka
5. Sergey I. Nikolenko
6. Qiang Wang
7. Markus Bayer
8. Sandeep Singh Sengar

Papers from Eyal Klang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. GENERATIVE ADVERSARIAL NETS
7. Dropout: a simple way to prevent neural networks from overfitting
8. Attention is All you Need (Neural Information Processing Systems)
9. Long Short-Term Memory
10. ImageNet: A large-scale hierarchical image database

Their papers

• Synthetic data augmentation using GAN for improved liver lesion classification (2018) — 847 citations
• The Liver Tumor Segmentation Benchmark (LiTS) (2019) — 1,500 citations
• GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification (2018) — 1,883 citations
• Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide. (2019) — 469 citations