Shan Suthaharan

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 4 papers; trusts 39 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 10 citations into it; #7,827 of 21,575 by trust here, #12,689 by citations
• Artificial Intelligence — 9 citations into it; #8,970 of 35,345 by trust here, #13,962 by citations
• Statistics and Probability — 8 citations into it
• Optimization & Training — 5 citations into it

Whom Shan Suthaharan trusts overall

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

1. Sebastian Thrun
2. Daniel L. Silver
3. Tom White
4. Zdzislaw Pawlak
5. Yoshua Bengio
6. Bernhard E. Boser
7. Vladimir Naumovich Vapnik
8. Geoffrey E. Hinton
9. Aaron C. Courville
10. Robert Tibshirani

Who trusts Shan Suthaharan overall

1. Ankit Thakkar
2. Ritika Lohiya
3. S. Rajaraman
4. Haidi Rao
5. Sameer Kiran Antani
6. Xianzhang Shi
7. Hoang Nguyen
8. Syeda Manjia Tahsien
9. Andrea De Mauro
10. Alexandra L’Heureux

Papers from Shan Suthaharan's perspective

Ordered by how strongly the researchers Shan Suthaharan 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Learning Models (2016) — 80 citations
• Support Vector Machine (2016) — 346 citations
• Machine Learning Models and Algorithms for Big Data Classification (2016) — 533 citations
• Big data classification: problems and challenges in network intrusion prediction with machine learning (2014) — 358 citations