Sutharshan Rajasegarar

Artificial Intelligence · 2007–2016

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #22,482 of 35,345 by trust here, #12,761 by citations
• Computer Vision and Pattern Recognition — 18 citations into it; #14,759 of 21,575 by trust here, #9,435 by citations
• Electrical and Electronic Engineering — 13 citations into it
• Computer Networks and Communications — 11 citations into it; #5,811 of 9,886 by trust here, #2,242 by citations
• Statistical Learning Theory — 6 citations into it
• Optimization & Training — 5 citations into it
• Signal Processing — 5 citations into it

Whom Sutharshan Rajasegarar trusts overall

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

1. Geoffrey E. Hinton
2. Bernhard Schölkopf
3. Yoshua Bengio
4. Yuhai Wu
5. Robert Szewczyk
6. Yann LeCun
7. Alex Smola
8. Pavel Laskov
9. Chih-Jen Lin
10. Sharmila Subramaniam

Who trusts Sutharshan Rajasegarar overall

1. Ismail Butun
2. Yang Zhang
3. Sarah Monazam Erfani
4. Mohammad Abu Alsheikh
5. Lukas Ruff
6. Zubair Muhammad Fadlullah
7. Raghavendra Chalapathy
8. Nivedita Mishra
9. Salvatore Domenic Morgera
10. Nirvana Meratnia

Papers from Sutharshan Rajasegarar's perspective

Ordered by how strongly the researchers Sutharshan Rajasegarar 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning (2016) — 1,120 citations
• Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey (2012) — 963 citations
• Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks (2007) — 213 citations