Jenifer Siegelman

Computer Vision and Pattern Recognition · 2013–2020

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

• Computer Vision and Pattern Recognition — 28 citations into it
• Artificial Intelligence — 19 citations into it
• Model Architectures — 7 citations into it

Whom Jenifer Siegelman trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Linda Wang
4. Alex Krizhevsky
5. François Chollet
6. Russell Reed
7. Christian Szegedy
8. Ilya Sutskever
9. Ross B. Girshick
10. Yoshua Bengio

Papers from Jenifer Siegelman's perspective

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

Their papers

• Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays (2020) — 299 citations
• Automatic Tuberculosis Screening Using Chest Radiographs (2013) — 689 citations