Vijay Mahadevan

Computer Vision and Pattern Recognition · 2008–2019

A researcher in the Science corpus: 7 papers; trusts 333 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 101 citations into it; #4,111 of 21,575 by trust here, #4,781 by citations
• Artificial Intelligence — 88 citations into it; #5,912 of 35,345 by trust here, #7,531 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Cognitive Neuroscience — 16 citations into it
• Signal Processing — 16 citations into it
• Model Architectures — 14 citations into it
• Reinforcement Learning — 11 citations into it
• Statistics and Probability — 11 citations into it

Whom Vijay Mahadevan trusts overall

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

1. Laurent Itti
2. Geoffrey E. Hinton
3. Gianfranco Doretto
4. Alex Krizhevsky
5. Mubarak Shah
6. Yoshua Bengio
7. Yann LeCun
8. Radford M. Neal
9. Diederik P. Kingma
10. John R. Hershey

Who trusts Vijay Mahadevan overall

1. Wenguan Wang
2. Radu Tudor Ionescu
3. Weixin Luo
4. Vishwanath A. Sindagi
5. Jianbing Shen
6. Trong-Nguyen Nguyen
7. Wen Liu
8. Dan Xu
9. Hyunjong Park
10. Davide Abati

Papers from Vijay Mahadevan's perspective

Ordered by how strongly the researchers Vijay Mahadevan 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Anomaly detection in crowded scenes (2010) — 1,581 citations
• Toward Understanding Catastrophic Forgetting in Continual Learning (2019) — 124 citations
• Anomaly Detection and Localization in Crowded Scenes (2014) — 1,101 citations
• Spatiotemporal Saliency in Dynamic Scenes (2010) — 437 citations
• Deep convolutional neural networks for eigenvalue problems in mechanics (2018) — 73 citations
• Cluster Canonical Correlation Analysis (2014) — 170 citations
• On the plausibility of the discriminant center-surround hypothesis for visual saliency. (2008) — 312 citations