Anil Jayanti Aswani

Artificial Intelligence · 2011–2018

A researcher in the Science corpus: 4 papers; trusts 74 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it
• Statistics and Probability — 31 citations into it; #11,727 of 12,932 by trust here, #7,165 by citations
• Safety, Robustness & Interpretability — 17 citations into it
• Control and Systems Engineering — 15 citations into it; #4,087 of 8,257 by trust here, #2,447 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #19,568 of 21,575 by trust here, #19,310 by citations
• Computational Mechanics — 12 citations into it; #8,955 of 10,001 by trust here, #8,258 by citations
• Optimization & Training — 10 citations into it
• Safety Research — 10 citations into it

Whom Anil Jayanti Aswani trusts overall

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

1. Robert Tibshirani
2. Peter J. Bickel
3. Iain M. Johnstone
4. Nicolai Meinshausen
5. Boaz Nadler
6. Hui Zou
7. Jerome H. Friedman
8. Trevor J. Hastie
9. Elizaveta Levina
10. Juha Reunanen

Who trusts Anil Jayanti Aswani overall

1. Jeremy H. Gillula
2. C. Ostafew
3. Claire Jennifer Tomlin
4. Lukas Hewing
5. Julian Berberich
6. Sarah Dean
7. Angela P. Schoellig
8. Matt Olfat
9. Felix Berkenkamp
10. Jaime Fernández Fisac

Papers from Anil Jayanti Aswani's perspective

Ordered by how strongly the researchers Anil Jayanti Aswani 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. ImageNet: A large-scale hierarchical image database
7. Regression Shrinkage and Selection via the Lasso
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Spectral Algorithms for Computing Fair Support Vector Machines (2017) — 36 citations
• Convex Formulations for Fair Principal Component Analysis (2018) — 55 citations
• Provably safe and robust learning-based model predictive control (2011) — 575 citations
• Regression on manifolds: Estimation of the exterior derivative (2011) — 77 citations