Syed Shakib Sarwar

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 153 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #14,448 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Electrical and Electronic Engineering — 25 citations into it; #13,830 of 16,539 by trust here, #12,727 by citations
• Cognitive Neuroscience — 20 citations into it; #9,106 of 10,852 by trust here, #8,363 by citations
• Model Architectures — 14 citations into it
• Optimization & Training — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Meta & Continual Learning — 7 citations into it

Whom Syed Shakib Sarwar trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. X. Zhang
8. Karen Simonyan
9. Andrea Vedaldi
10. Li Fei-Fei

Who trusts Syed Shakib Sarwar overall

1. Onur Tasar
2. Umberto Michieli
3. Yuliya Tarabalka
4. Pietro Zanuttigh
5. Yu Gan
6. Nassim Ammour
7. Pierre Alliez
8. Wei Fang
9. Kaushik Roy
10. Gobinda Saha

Papers from Syed Shakib Sarwar's perspective

Ordered by how strongly the researchers Syed Shakib Sarwar 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. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures (2019) — 452 citations
• Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing (2017) — 118 citations