Bilal Alsallakh

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 244 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it
• Artificial Intelligence — 55 citations into it
• Safety, Robustness & Interpretability — 26 citations into it
• Model Architectures — 17 citations into it
• Biophysics — 11 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Bilal Alsallakh trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Karen Simonyan
4. Andrea Vedaldi
5. David G. Lowe
6. Kaiming He
7. Christian Szegedy
8. Yann LeCun
9. Andrew Zisserman
10. Yoshua Bengio

Papers from Bilal Alsallakh's perspective

Ordered by how strongly the researchers Bilal Alsallakh 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Mind the Pad - CNNs can Develop Blind Spots (2020) — 85 citations
• Captum: A unified and generic model interpretability library for PyTorch (2020) — 1,169 citations
• Do Convolutional Neural Networks Learn Class Hierarchy? (2017) — 232 citations