Gopalakrishnan Srinivasan

Electrical and Electronic Engineering · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 96 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 44 citations into it; #2,523 of 16,539 by trust here, #8,322 by citations
• Cognitive Neuroscience — 40 citations into it; #2,322 of 10,852 by trust here, #5,162 by citations
• Artificial Intelligence — 30 citations into it; #32,151 of 35,345 by trust here, #26,682 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 10 citations into it
• Cellular and Molecular Neuroscience — 9 citations into it; #1,041 of 2,998 by trust here, #2,655 by citations

Whom Gopalakrishnan Srinivasan trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Peter U. Diehl
4. Kaiming He
5. Karen Simonyan
6. Paul J. Werbos
7. Sumit Bam Shrestha
8. Eric Hunsberger
9. Yann LeCun
10. Sergey Ioffe

Who trusts Gopalakrishnan Srinivasan overall

1. Wei Fang
2. Nitin Rathi
3. Zhaofei Yu
4. Yanqing Chen
5. Kaushik Roy
6. Shi-Wee Deng
7. Yonghong Tian
8. Timothée Masquelier
9. Tiejun Huang
10. Man Yao

Papers from Gopalakrishnan Srinivasan's perspective

Ordered by how strongly the researchers Gopalakrishnan Srinivasan 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures (2019) — 452 citations
• RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network (2020) — 411 citations
• Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation (2020) — 356 citations