David I. Inouye

Artificial Intelligence · with Ravikumar · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 34 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it
• Safety, Robustness & Interpretability — 64 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it
• Biophysics — 13 citations into it
• Natural Language Processing — 8 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom David I. Inouye trusts overall

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

1. Matthew D. Zeiler
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Fabian Pedregosa
5. Yoshua Bengio
6. Karen Simonyan
7. Yann LeCun
8. Diederik P. Kingma
9. Rob Fergus
10. Kaiming He

Papers from David I. Inouye's perspective

Ordered by how strongly the researchers David I. Inouye 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. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. ImageNet: A large-scale hierarchical image database
10. Going deeper with convolutions

Their papers

• How Sensitive are Sensitivity-Based Explanations? (2019) — 23 citations
• On the (In)fidelity and Sensitivity for Explanations. (2019) — 604 citations
• Hyperparameter Selection under Localized Label Noise via Corrupt Validation (2017)