Shashank Kotyan

Artificial Intelligence · 2019

A researcher in the Science corpus: 3 papers; trusts 364 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #31,826 of 35,345 by trust here, #32,362 by citations
• Safety, Robustness & Interpretability — 72 citations into it
• Computer Vision and Pattern Recognition — 54 citations into it
• Model Architectures — 18 citations into it
• Hardware and Architecture — 15 citations into it
• Signal Processing — 13 citations into it
• Molecular Biology — 11 citations into it
• Neural Architecture Search & AutoML — 9 citations into it

Whom Shashank Kotyan trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Ian J. Goodfellow
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Yann LeCun
7. Yoshua Bengio
8. Ilya Sutskever
9. Diederik P. Kingma
10. Danilo Vasconcellos Vargas

Who trusts Shashank Kotyan overall

1. Danilo Vasconcellos Vargas

Papers from Shashank Kotyan's perspective

Ordered by how strongly the researchers Shashank Kotyan 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Going deeper with convolutions

Their papers

• Model Agnostic Dual Quality Assessment for Adversarial Machine Learning and an Analysis of Current Neural Networks and Defenses (2019) — 8 citations
• Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary (2019) — 20 citations
• Evolving Robust Neural Architectures to Defend from Adversarial Attacks (2019) — 37 citations