Isaac Lage

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 18 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it
• Safety, Robustness & Interpretability — 39 citations into it; #2,008 of 2,251 by trust here, #1,819 by citations
• Information Systems — 16 citations into it
• Statistical Learning Theory — 8 citations into it
• Natural Language Processing — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Isaac Lage trusts overall

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

1. William W. Cohen
2. Geoffrey E. Hinton
3. Finale Doshi-Velez
4. Himabindu Lakkaraju
5. Alex Alves Freitas
6. Peter Clark
7. Marco Tulio Ribeiro
8. Eibe Frank
9. R. Caruana
10. Niko Speybroeck

Who trusts Isaac Lage overall

1. Forough Poursabzi-Sangdeh
2. Giulia Vilone
3. Sina Mohseni
4. Xinru Wang
5. Guang Yang
6. A. Yeung
7. Hendrik Schuff
8. Daniel G. Goldstein
9. Longo Luca
10. Wojciech Samek

Papers from Isaac Lage's perspective

Ordered by how strongly the researchers Isaac Lage 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018) — 151 citations
• An Evaluation of the Human-Interpretability of Explanation (2019) — 186 citations
• Human Evaluation of Models Built for Interpretability (2019) — 152 citations
• Human-in-the-Loop Interpretability Prior (2018) — 129 citations