Elias B. Issa

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 77 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it
• Cognitive Neuroscience — 56 citations into it; #6,129 of 10,852 by trust here, #2,549 by citations
• Artificial Intelligence — 43 citations into it
• Model Architectures — 27 citations into it
• Neuroscience — 17 citations into it
• Optimization & Training — 9 citations into it
• Biophysics — 7 citations into it
• Media Technology — 5 citations into it

Whom Elias B. Issa trusts overall

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

1. Alex Krizhevsky
2. Christian Szegedy
3. Kaiming He
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Ilya Sutskever
7. Yoshua Bengio
8. Du Q. Huynh
9. Daniel L. K. Yamins
10. Nicolas Pinto

Who trusts Elias B. Issa overall

1. Martin Schrimpf
2. Robert Geirhos
3. Pouya Bashivan
4. Jonas Kubilius
5. Grace W. Lindsay
6. Chengxu Zhuang

Papers from Elias B. Issa's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. ImageNet: A large-scale hierarchical image database
4. ImageNet Large Scale Visual Recognition Challenge
5. Going deeper with convolutions
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Long Short-Term Memory

Their papers

• Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks (2018) — 355 citations
• Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs (2019) — 333 citations
• Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior (2018) — 488 citations
• Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? (2018) — 725 citations