Ambrish Rawat

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 439 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 216 citations into it; #9,440 of 35,345 by trust here, #9,968 by citations
• Safety, Robustness & Interpretability — 121 citations into it; #692 of 2,251 by trust here, #1,312 by citations
• Computer Vision and Pattern Recognition — 104 citations into it; #17,142 of 21,575 by trust here, #17,258 by citations
• Signal Processing — 33 citations into it; #2,008 of 7,572 by trust here, #2,343 by citations
• Model Architectures — 26 citations into it
• Neural Architecture Search & AutoML — 26 citations into it; #217 of 283 by trust here, #250 by citations
• Optimization & Training — 26 citations into it
• Probabilistic & Bayesian Methods — 23 citations into it

Whom Ambrish Rawat trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yann LeCun
4. Yoshua Bengio
5. Diederik P. Kingma
6. Alex Graves
7. Christian Szegedy
8. Ian J. Goodfellow
9. Kaiming He
10. Ilya Sutskever

Who trusts Ambrish Rawat overall

1. Kuan-Chieh Jackson Wang
2. David Stutz
3. Nicholas Carlini
4. Xuanqing Liu
5. Yi-Wei Chen
6. Maria-Irina Nicolae
7. Haifeng Qian
8. Yuqiao Liu
9. Pin-Yu Chen
10. Huan Zhang

Papers from Ambrish Rawat's perspective

Ordered by how strongly the researchers Ambrish Rawat 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Adversarial Phenomenon in the Eyes of Bayesian Deep Learning (2017) — 42 citations
• Efficient Defenses Against Adversarial Attacks (2017) — 315 citations
• Adversarial Robustness Toolbox v1.0.0 (2018) — 573 citations
• IBM Federated Learning: an Enterprise Framework White Paper V0.1 (2020) — 198 citations
• A Survey on Neural Architecture Search (2019) — 293 citations
• Adversarial Robustness Toolbox v0.2.2 (2018) — 59 citations