Omar Shaikh

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 5 papers; trusts 40 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 164 citations into it
• Natural Language Processing — 71 citations into it
• Large Language Models — 48 citations into it
• Safety, Robustness & Interpretability — 44 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Computer Science Applications — 9 citations into it
• Human-Computer Interaction — 7 citations into it

Whom Omar Shaikh trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Ilya Sutskever
7. Yann LeCun
8. Quoc V. Le
9. Karen Simonyan
10. Tomas Mikolov

Who trusts Omar Shaikh overall

1. Alexander Wei
2. Jesse Dodge
3. Nika Haghtalab

Papers from Omar Shaikh's perspective

Ordered by how strongly the researchers Omar Shaikh 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models (2021) — 29 citations
• Can Large Language Models Transform Computational Social Science? (2023) — 564 citations
• On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning (2022) — 283 citations
• CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization (2020) — 318 citations
• RECAST: Interactive Auditing of Automatic Toxicity Detection Models (2020) — 7 citations