Vineet Gupta

Artificial Intelligence · with Anil · 2004–2021

A researcher in the Science corpus: 5 papers; trusts 97 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #23,186 of 35,345 by trust here, #15,004 by citations
• Optimization & Training — 39 citations into it; #1,612 of 2,601 by trust here, #1,638 by citations
• Computer Vision and Pattern Recognition — 33 citations into it
• Natural Language Processing — 16 citations into it
• Management Science and Operations Research — 15 citations into it
• Computational Mechanics — 11 citations into it; #2,384 of 10,001 by trust here, #3,126 by citations
• Computer Networks and Communications — 10 citations into it
• Privacy & Security — 10 citations into it

Whom Vineet Gupta trusts overall

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

1. Diederik P. Kingma
2. Elad Hazan
3. Shai Shalev-Shwartz
4. Geoffrey E. Hinton
5. John C. Duchi
6. Jimmy Ba
7. Yoshua Bengio
8. Jorge Nocedal
9. James Martens
10. Kaiming He

Who trusts Vineet Gupta overall

1. Philip M. Long
2. Rohan Anil
3. Etai Littwin
4. Hanie Sedghi
5. Behnam Neyshabur
6. Xinyi Chen
7. Zi-Mei Yao
8. Luke Metz
9. Naman Agarwal
10. Lior Wolf

Papers from Vineet Gupta's perspective

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

Their papers

• Shampoo: Preconditioned Stochastic Tensor Optimization (2018) — 492 citations
• Large-Scale Differentially Private BERT (2021) — 162 citations
• Metrics for labelled Markov processes (2004) — 363 citations
• The Singular Values of Convolutional Layers (2018) — 225 citations
• Memory-Efficient Adaptive Optimization for Large-Scale Learning (2019) — 50 citations