James Sharp

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it
• Safety, Robustness & Interpretability — 92 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Software — 15 citations into it
• Electrical and Electronic Engineering — 14 citations into it
• Program Analysis & Verification — 11 citations into it

Whom James Sharp trusts overall

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

1. Ian J. Goodfellow
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yann LeCun
5. Yoshua Bengio
6. Alex Krizhevsky
7. Christian Szegedy
8. Xiaowei Huang
9. Kaiming He
10. Alessio R. Lomuscio

Papers from James Sharp's perspective

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

Their papers

• A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability (2018) — 607 citations
• A Safety Framework for Critical Systems Utilising Deep Neural Networks (2020) — 52 citations