Rohit Tripathy

Statistics and Probability · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 75 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 35 citations into it; #3,518 of 12,932 by trust here, #3,609 by citations
• Artificial Intelligence — 34 citations into it; #26,007 of 35,345 by trust here, #24,417 by citations
• Optimization & Training — 18 citations into it; #1,202 of 2,601 by trust here, #1,766 by citations
• Computational Theory and Mathematics — 17 citations into it; #3,589 of 12,182 by trust here, #4,850 by citations
• Statistical and Nonlinear Physics — 14 citations into it; #2,041 of 10,127 by trust here, #2,928 by citations
• Computer Vision and Pattern Recognition — 13 citations into it
• Model Architectures — 12 citations into it
• Management Science and Operations Research — 11 citations into it

Whom Rohit Tripathy trusts overall

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

1. Diederik P. Kingma
2. Alex Graves
3. Geoffrey E. Hinton
4. Matthew D. Zeiler
5. Yoshua Bengio
6. L. Eon Bottou
7. Charles M. Bishop
8. Yann LeCun
9. Alex Krizhevsky
10. Simon S. Haykin

Who trusts Rohit Tripathy overall

1. Constantin Grigo
2. Jared D. Willard
3. Sharmila Karumuri
4. Shaoxing Mo
5. Luning Sun
6. Phaedon-Stelios Koutsourelakis
7. Han Gao
8. Yinhao Zhu
9. Nicholas Zabaras
10. Xiaowei Jia

Papers from Rohit Tripathy's perspective

Ordered by how strongly the researchers Rohit Tripathy 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (2018) — 462 citations
• Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (2019) — 154 citations