Abhishek Singh

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 673 researchers and is trusted by 214 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 390 citations into it
• Computer Vision and Pattern Recognition — 170 citations into it; #2,524 of 21,575 by trust here, #4,584 by citations
• Privacy & Security — 90 citations into it
• Federated & Distributed Learning — 86 citations into it
• Safety, Robustness & Interpretability — 63 citations into it
• Model Architectures — 33 citations into it
• Electrical and Electronic Engineering — 31 citations into it
• Optimization & Training — 31 citations into it

Whom Abhishek Singh 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. Karen Simonyan
6. Cynthia Dwork
7. Yann LeCun
8. Diederik P. Kingma
9. Christian Szegedy
10. Ilya Sutskever

Who trusts Abhishek Singh overall

1. Yulun Zhang
2. Radu Timofte
3. Yochai Blau
4. Jinjin Gu
5. Saeed Anwar
6. Eirikur Agustsson
7. Kai Zhang
8. Yong Guo
9. Chao Dong
10. Ding Liu

Papers from Abhishek Singh's perspective

Ordered by how strongly the researchers Abhishek Singh trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Single image super-resolution from transformed self-exemplars (2015) — 3,414 citations
• Detailed comparison of communication efficiency of split learning and federated learning (2019) — 222 citations
• DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks (2020) — 63 citations
• SplitNN-driven Vertical Partitioning (2020) — 91 citations
• Privacy in Deep Learning: A Survey (2020) — 153 citations
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020) — 716 citations
• NoPeek: Information leakage reduction to share activations in distributed deep learning (2020) — 119 citations
• Fast Neural Architecture Construction using EnvelopeNets (2018) — 7 citations
• Neural Architecture Construction using EnvelopeNets (2018) — 14 citations