Ethan M. Rudd

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 175 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #8,264 of 35,345 by trust here, #10,798 by citations
• Computer Vision and Pattern Recognition — 54 citations into it; #6,292 of 21,575 by trust here, #8,296 by citations
• Computer Networks and Communications — 26 citations into it
• Signal Processing — 22 citations into it
• Model Architectures — 10 citations into it
• Safety, Robustness & Interpretability — 10 citations into it; #687 of 2,251 by trust here, #1,404 by citations
• Probabilistic & Bayesian Methods — 9 citations into it
• Media Technology — 8 citations into it

Whom Ethan M. Rudd trusts overall

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

1. Walter J. Scheirer
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yann LeCun
5. Radford M. Neal
6. Lalit P. Jain
7. Christian Szegedy
8. Terrance E. Boult
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Ethan M. Rudd overall

1. Andras Rozsa
2. Terrance E. Boult
3. Manuel Günther
4. Richard E. Harang
5. Chuanxing Geng
6. Edoardo Vignotto
7. Akshay Raj Dhamija
8. Nicholas Carlini
9. Naveed Akhtar
10. Jihong Park

Papers from Ethan M. Rudd's perspective

Ordered by how strongly the researchers Ethan M. Rudd 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Adversarial Diversity and Hard Positive Generation (2016) — 268 citations
• Principled Uncertainty Estimation for Deep Neural Networks (2018) — 3 citations
• A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016) — 166 citations
• The Extreme Value Machine (2015) — 346 citations