Neil Shah

Artificial Intelligence · AAAI Conference on Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 86 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it
• Information Systems — 54 citations into it
• Sociology and Political Science — 45 citations into it
• Statistical and Nonlinear Physics — 43 citations into it
• Graph Learning — 34 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it
• Communication — 14 citations into it
• Computer Networks and Communications — 12 citations into it

Whom Neil Shah trusts overall

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

1. Jure Leskovec
2. William Yang Wang
3. Geoffrey E. Hinton
4. Myle Ott
5. Nitin Jindal
6. Yoshua Bengio
7. Diederik P. Kingma
8. B. Liu
9. Yann LeCun
10. Alex Krizhevsky

Papers from Neil Shah's perspective

Ordered by how strongly the researchers Neil Shah 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. GENERATIVE ADVERSARIAL NETS
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Is Homophily a Necessity for Graph Neural Networks? (2021) — 314 citations
• False Information on Web and Social Media: A Survey (2018) — 396 citations
• FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (2016) — 393 citations
• Data Augmentation for Graph Neural Networks (2020) — 507 citations