Ajay Basavanhally

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 40 citations into it; #17,964 of 21,575 by trust here, #8,831 by citations
• Artificial Intelligence — 36 citations into it; #29,144 of 35,345 by trust here, #9,870 by citations
• Representation & Self-Supervised Learning — 8 citations into it

Whom Ajay Basavanhally trusts overall

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

1. Yoshua Bengio
2. Yann LeCun
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Koray Kavukcuoglu
6. L. Eon Bottou
7. Quoc V. Le
8. Ilya Sutskever
9. Ronan Collobert
10. Marc'Aurelio Ranzato

Who trusts Ajay Basavanhally overall

1. Andrew Janowczyk
2. Anant Madabhushi
3. Chetan L. Srinidhi

Papers from Ajay Basavanhally's perspective

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

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

Their papers

• Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks (2014) — 632 citations
• Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent (2017) — 479 citations
• Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features (2014) — 334 citations