Ravi K. Samala

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 96 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #33,036 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 43 citations into it; #20,274 of 21,575 by trust here, #14,151 by citations
• Optimization & Training — 13 citations into it
• Model Architectures — 12 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Safety, Robustness & Interpretability — 8 citations into it
• Biophysics — 5 citations into it

Whom Ravi K. Samala trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Ilya Sutskever
5. Yann LeCun
6. Kaiming He
7. Ruslan Salakhutdinov
8. Diederik P. Kingma
9. Nitish Srivastava
10. Matthew D. Zeiler

Who trusts Ravi K. Samala overall

1. Kenji Suzuki
2. Heang-Ping Chan
3. Berkman Sahiner
4. Maciej A. Mazurowski
5. Lubomir M. Hadjiiski
6. Abeer Saber
7. Geert J. S. Litjens
8. Ghulam Murtaza
9. Ken Chang
10. Arvid Lundervold

Papers from Ravi K. Samala's perspective

Ordered by how strongly the researchers Ravi K. Samala 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. (2016) — 247 citations
• Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI (2020) — 69 citations
• Computer-Aided Diagnosis in the Era of Deep Learning (2020) — 297 citations
• Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms (2017) — 203 citations
• Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. (2016) — 270 citations