Rathinakumar Appuswamy

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 6 papers; trusts 78 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 57 citations into it; #13,225 of 21,575 by trust here, #7,852 by citations
• Artificial Intelligence — 51 citations into it; #21,234 of 35,345 by trust here, #6,848 by citations
• Efficiency & Compression — 22 citations into it; #738 of 1,264 by trust here, #405 by citations
• Optimization & Training — 21 citations into it
• Electrical and Electronic Engineering — 19 citations into it
• Computer Networks and Communications — 10 citations into it

Whom Rathinakumar Appuswamy trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Matthieu Courbariaux
7. Ralf Koetter
8. Kaiming He
9. Ilya Sutskever
10. Jimmy Ba

Who trusts Rathinakumar Appuswamy overall

1. Shimeng Yu
2. Sumit Bam Shrestha
3. Michael Pfeiffer
4. El Mahdi El Mhamdi
5. Itay Hubara
6. Md Maruf Hossain Shuvo
7. Junhaeng Lee
8. Bodo Rueckauer
9. Markus Nagel
10. Matthieu Courbariaux

Papers from Rathinakumar Appuswamy's perspective

Ordered by how strongly the researchers Rathinakumar Appuswamy 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. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learned Step Size Quantization (2019) — 1,090 citations
• Deep neural networks are robust to weight binarization and other non-linear distortions (2016) — 97 citations
• Convolutional networks for fast, energy-efficient neuromorphic computing (2016) — 763 citations
• A million spiking-neuron integrated circuit with a scalable communication network and interface (2014) — 3,922 citations
• Computing Linear Functions by Linear Coding Over Networks (2011) — 51 citations
• Backpropagation for Energy-Efficient Neuromorphic Computing (2015) — 328 citations