Gabriel Erion-Barner

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 102 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 131 citations into it; #6,933 of 35,345 by trust here, #6,796 by citations
• Safety, Robustness & Interpretability — 63 citations into it; #605 of 2,251 by trust here, #1,038 by citations
• Computer Vision and Pattern Recognition — 54 citations into it
• Molecular Biology — 23 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Statistics and Probability — 16 citations into it
• Optimization & Training — 14 citations into it
• Model Architectures — 11 citations into it

Whom Gabriel Erion-Barner trusts overall

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

1. Scott M. Lundberg
2. Robert Tibshirani
3. Geoffrey E. Hinton
4. Yann LeCun
5. Alex Krizhevsky
6. Karen Simonyan
7. Christian Szegedy
8. Eric J. Friedman
9. Andrew Slavin Ross
10. Diederik P. Kingma

Who trusts Gabriel Erion-Barner overall

1. Joseph D. Janizek
2. Scott M. Lundberg
3. Su-In Lee
4. Patrick Hall
5. Ian Covert
6. Alex J. DeGrave
7. Guy Van den Broeck
8. Hugh Chen
9. Jonathan Brophy
10. Frederick Liu

Papers from Gabriel Erion-Barner's perspective

Ordered by how strongly the researchers Gabriel Erion-Barner 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. ImageNet Large Scale Visual Recognition Challenge
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Explainable AI for Trees: From Local Explanations to Global Understanding (2019) — 385 citations
• Improving performance of deep learning models with axiomatic attribution priors and expected gradients (2020) — 355 citations
• Learning Explainable Models Using Attribution Priors (2019) — 85 citations
• An adversarial approach for the robust classification of pneumonia from chest radiographs (2020) — 33 citations
• From Local Explanations to Global Understanding with Explainable AI for Trees (2020) — 8,322 citations
• Consistent Individualized Feature Attribution for Tree Ensembles (2018) — 1,890 citations