Mahesan Niranjan

Computer Vision and Pattern Recognition · 1990–2020

A researcher in the Science corpus: 8 papers; trusts 264 researchers and is trusted by 220 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 58 citations into it; #17,625 of 21,575 by trust here, #12,992 by citations
• Artificial Intelligence — 55 citations into it; #23,150 of 35,345 by trust here, #18,186 by citations
• Model Architectures — 23 citations into it
• Optimization & Training — 15 citations into it; #2,265 of 2,601 by trust here, #2,313 by citations
• Probabilistic & Bayesian Methods — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Mahesan Niranjan trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Radford M. Neal
5. Yann LeCun
6. Karen Simonyan
7. Yoshua Bengio
8. Ilya Sutskever
9. David John Cameron MacKay
10. Ross B. Girshick

Who trusts Mahesan Niranjan overall

1. Matthew E. Taylor
2. Peter Stone
3. Hado van Hasselt
4. J. Urgen Schmidhuber
5. Richard S. Sutton
6. Marc G. Bellemare
7. Kavosh Asadi
8. Satinder Singh
9. Sergey Levine
10. Yoshua Bengio

Papers from Mahesan Niranjan's perspective

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

Their papers

• Enriching Texture Analysis with Semantic Data (2013) — 33 citations
• FMix: Enhancing Mixed Sample Data Augmentation (2020) — 183 citations
• Sequential Monte Carlo Methods to Train Neural Network Models (2000) — 234 citations
• Deep Cascade Learning (2018) — 126 citations
• Sequential Monte Carlo Methods for Neural Networks (2001) — 44 citations
• Fully vector-quantized neural network-based code-excited nonlinear predictive speech coding (1994)
• On-line Q-learning using connectionist systems (1994)
• A theoretical investigation into the performance of the Hopfield model (1990)