Nishant Kumar

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 35 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #32,492 of 35,345 by trust here, #25,809 by citations
• Privacy & Security — 36 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it; #19,957 of 21,575 by trust here, #17,757 by citations
• Computational Theory and Mathematics — 12 citations into it
• Electrical and Electronic Engineering — 7 citations into it
• Hardware and Architecture — 7 citations into it
• ML Systems & Hardware — 7 citations into it
• Model Architectures — 7 citations into it

Whom Nishant Kumar trusts overall

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

1. Payman Mohassel
2. Kaiming He
3. Ran Canetti
4. Florian Tramèr
5. Yann LeCun
6. Oded Goldreich
7. Jia Deng
8. Daniel Demmler
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Nishant Kumar overall

1. Marcel Keller
2. Assi Barak
3. Bo Li
4. Brian Knott
5. Deevashwer Rathee
6. Zhicong Huang

Papers from Nishant Kumar's perspective

Ordered by how strongly the researchers Nishant Kumar 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. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• CrypTFlow: Secure TensorFlow Inference (2019) — 280 citations
• CrypTFlow2: Practical 2-Party Secure Inference (2020) — 450 citations