Mayank Agarwal

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 1 researcher and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #23,723 of 35,345 by trust here, #11,072 by citations

Whom Mayank Agarwal trusts overall

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

1. Thomas G. Dietterich
2. Mikhail Yurochkin
3. Michael I. Jordan
4. T. Griffiths
5. Geoffrey E. Hinton
6. Romain Thibaux
7. Zoubin Ghahramani
8. Trevor J. Hastie
9. Radford M. Neal
10. Yoshua Bengio

Who trusts Mayank Agarwal overall

1. Qinbin Li
2. Hongyi Wang
3. Mikhail Yurochkin
4. Jaehong Yoon
5. Hong-You Chen
6. Tzu-Ming Harry Hsu
7. Bingsheng He
8. Heiko Ludwig
9. Wonyong Jeong

Papers from Mayank Agarwal's perspective

Ordered by how strongly the researchers Mayank Agarwal 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Distilling the Knowledge in a Neural Network (arXiv.org)
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Bayesian Nonparametric Federated Learning of Neural Networks (2019) — 912 citations
• IBM Federated Learning: an Enterprise Framework White Paper V0.1 (2020) — 198 citations
• Statistical Model Aggregation via Parameter Matching (2019) — 38 citations