Ayesha Bajwa

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 134 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it
• Safety, Robustness & Interpretability — 73 citations into it
• Computer Vision and Pattern Recognition — 60 citations into it
• Biophysics — 12 citations into it
• Natural Language Processing — 12 citations into it
• Model Architectures — 10 citations into it
• Computational Theory and Mathematics — 8 citations into it

Whom Ayesha Bajwa trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Matthew D. Zeiler
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Quanshi Zhang
7. Yoshua Bengio
8. Yann LeCun
9. Bolei Zhou
10. Karen Simonyan

Papers from Ayesha Bajwa's perspective

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

Their papers

• Explaining Explanations: An Overview of Interpretability of Machine Learning (2018) — 2,256 citations
• Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018) — 187 citations