Vinay Rao

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 4 papers; trusts 44 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #29,547 of 35,345 by trust here, #13,962 by citations
• Natural Language Processing — 44 citations into it; #3,658 of 5,688 by trust here, #782 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• Model Architectures — 24 citations into it
• Optimization & Training — 21 citations into it; #2,019 of 2,601 by trust here, #1,468 by citations
• Evaluation & Benchmarks — 10 citations into it; #2,556 of 3,147 by trust here, #1,895 by citations
• Scaling & Learning Theory — 10 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Vinay Rao trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Makoto Miwa
4. Alex Graves
5. Noam Shazeer
6. Chin-Yew Lin
7. Ilya Sutskever
8. Diederik P. Kingma
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Vinay Rao overall

1. Greg Yang
2. Vidhisha Balachandran
3. Esin Durmus
4. Alex Wang
5. Artidoro Pagnoni
6. Mengyao Cao
7. Wojciech Kryscinski
8. Zhiyuan Zeng
9. Joshua Maynez
10. Wenhu Chen

Papers from Vinay Rao's perspective

Ordered by how strongly the researchers Vinay Rao 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Assessing The Factual Accuracy of Generated Text (2019) — 219 citations
• Active Learning for Speech Recognition: the Power of Gradients (2016) — 69 citations
• A Mean Field Theory of Batch Normalization (2019) — 192 citations
• Deep Speech: Scaling up end-to-end speech recognition (2014) — 2,263 citations