Tina Kapur

Artificial Intelligence · 2009–2019

A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Optimization & Training — 8 citations into it
• Safety, Robustness & Interpretability — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Model Architectures — 5 citations into it

Whom Tina Kapur trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Sergey Ioffe
5. Jimmy Ba
6. Alex Krizhevsky
7. Christian Szegedy
8. X. Zhang
9. Yoshua Bengio
10. Yann LeCun

Papers from Tina Kapur's perspective

Ordered by how strongly the researchers Tina Kapur trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Long Short-Term Memory
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• OpenIGTLink: an open network protocol for image-guided therapy environment (2009) — 367 citations
• Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation (2019) — 390 citations
• Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation (2017) — 391 citations