Andrew Slavin Ross

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 95 researchers and is trusted by 82 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #3,033 of 35,345 by trust here, #5,424 by citations
• Safety, Robustness & Interpretability — 41 citations into it; #171 of 2,251 by trust here, #401 by citations
• Computer Vision and Pattern Recognition — 30 citations into it
• Management Science and Operations Research — 21 citations into it
• Signal Processing — 14 citations into it
• Statistical and Nonlinear Physics — 12 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Natural Language Processing — 9 citations into it

Whom Andrew Slavin Ross trusts overall

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

1. Diederik P. Kingma
2. Yann LeCun
3. Ian J. Goodfellow
4. Geoffrey E. Hinton
5. Jimmy Ba
6. Nicolas Papernot
7. Yoshua Bengio
8. Alex Krizhevsky
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Andrew Slavin Ross overall

1. Gabriel Erion-Barner
2. David Stutz
3. Joost R. van Amersfoort
4. Gagan Bansal
5. Sina Mohseni
6. Naveed Akhtar
7. Chih-Kuan Yeh
8. Leilani H. Gilpin
9. Daniel Jakubovitz
10. Wojciech Samek

Papers from Andrew Slavin Ross's perspective

Ordered by how strongly the researchers Andrew Slavin Ross 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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations (2017) — 691 citations
• Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients (2017) — 754 citations
• Tackling Climate Change with Machine Learning (2019) — 1,184 citations
• Human-in-the-Loop Interpretability Prior (2018) — 129 citations
• Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning (2018) — 110 citations