Rohan Dharmakumar

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 31 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 54 citations into it

Whom Rohan Dharmakumar trusts

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

1. Andrew Zisserman (in Computer Vision and Pattern Recognition)
2. Jitendra Malik (in Computer Vision and Pattern Recognition)
3. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
4. Yann LeCun (in Computer Vision and Pattern Recognition)
5. Ilya Sutskever (in Computer Vision and Pattern Recognition)
6. Kaiming He (in Computer Vision and Pattern Recognition)
7. Ross B. Girshick (in Computer Vision and Pattern Recognition)
8. Trevor Darrell (in Computer Vision and Pattern Recognition)
9. Yoshua Bengio (in Computer Vision and Pattern Recognition)
10. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)

Papers from Rohan Dharmakumar's perspective

Ordered by how strongly the researchers Rohan Dharmakumar 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
8. Stochastic Backpropagation and Approximate Inference in Deep Generative Models
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Disentangled representation learning in cardiac image analysis (2019) — 168 citations
• Factorised spatial representation learning: application in semi-supervised myocardial segmentation (2018) — 77 citations
• Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (2015) — 647 citations