Shankar Krishnan

Artificial Intelligence · with Ghorbani · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 27 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #21,458 of 35,345 by trust here, #17,110 by citations
• Optimization & Training — 38 citations into it; #1,796 of 2,601 by trust here, #1,537 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #13,335 of 21,575 by trust here, #12,172 by citations
• Mechanical Engineering — 19 citations into it
• Scaling & Learning Theory — 12 citations into it
• Model Architectures — 9 citations into it
• Numerical Methods & Scientific Computing — 7 citations into it
• Statistical and Nonlinear Physics — 7 citations into it

Whom Shankar Krishnan trusts overall

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

1. M. Jaworski
2. Karen Simonyan
3. Barak A. Pearlmutter
4. Vardan Papyan
5. Gene H. Golub
6. Sergey Ioffe
7. David R. Lide
8. Diederik P. Kingma
9. Christian Szegedy
10. Geoffrey E. Hinton

Who trusts Shankar Krishnan overall

1. Namuk Park
2. Shuangfei Zhai
3. Zi-Mei Yao
4. Stanislav Fort
5. Pierre Foret
6. Stanislaw Jastrzebski

Papers from Shankar Krishnan's perspective

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

Their papers

• An Investigation into Neural Net Optimization via Hessian Eigenvalue Density (2019) — 460 citations
• Adaptive Gradient Methods at the Edge of Stability (2022) — 86 citations
• A Review On Transient Thermal Management of Electronic Devices (2021) — 212 citations