Lalana Kagal

Artificial Intelligence · 2003–2020

A researcher in the Science corpus: 7 papers; trusts 142 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #12,528 of 35,345 by trust here, #7,172 by citations
• Safety, Robustness & Interpretability — 78 citations into it; #860 of 2,251 by trust here, #500 by citations
• Computer Vision and Pattern Recognition — 66 citations into it
• Safety Research — 13 citations into it; #1,491 of 5,829 by trust here, #3,346 by citations
• Biophysics — 12 citations into it
• Information Systems — 12 citations into it; #6,081 of 8,771 by trust here, #4,682 by citations
• Natural Language Processing — 12 citations into it
• AI Ethics, Fairness & Society — 10 citations into it; #162 of 494 by trust here, #352 by citations

Whom Lalana Kagal trusts overall

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

1. Toon Calders
2. Geoffrey E. Hinton
3. Kaiming He
4. Toshihiro Kamishima
5. Matthew D. Zeiler
6. Alex Krizhevsky
7. Mahadev Satyanarayanan
8. Quanshi Zhang
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Lalana Kagal overall

1. Harry Chen
2. Sarah Tan
3. Timothy W. Finin
4. Ning Xie
5. Michele Donini
6. Riccardo Guidotti
7. Filip Perich
8. Luca Oneto
9. Anupam Joshi
10. Donovan Artz

Papers from Lalana Kagal's perspective

Ordered by how strongly the researchers Lalana Kagal trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Explaining Explanations: An Overview of Interpretability of Machine Learning (2018) — 2,256 citations
• Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models (2016) — 68 citations
• A Policy Based Approach to Security for the Semantic Web (2003) — 349 citations
• Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence (2020) — 486 citations
• A policy language for a pervasive computing environment (2003) — 632 citations
• Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018) — 187 citations
• Intelligent Agents Meet the Semantic Web in Smart Spaces (2004) — 312 citations