Kacper Sokol

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 34 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #20,376 of 35,345 by trust here, #14,718 by citations
• Safety, Robustness & Interpretability — 47 citations into it
• AI Ethics, Fairness & Society — 13 citations into it
• Safety Research — 13 citations into it; #2,654 of 5,829 by trust here, #2,686 by citations
• Health Informatics — 7 citations into it
• Causal Inference & Discovery — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Natural Language Processing — 5 citations into it

Whom Kacper Sokol trusts overall

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

1. Tim Miller
2. Tania Lombrozo
3. Todd Kulesza
4. Sandra Wachter
5. Marco Tulio Ribeiro
6. Mark W. Craven
7. Daniel S. Weld
8. Thibault Laugel
9. Been Kim
10. Jelena Luketina

Who trusts Kacper Sokol overall

1. Ilia Stepin
2. Yanou Ramon
3. Amir-Hossein Karimi
4. J. Alonso
5. Sarah Wiegreffe
6. Sahil Verma
7. David Martens
8. Alejandro Catalá
9. Q. Liao
10. Timo Speith

Papers from Kacper Sokol's perspective

Ordered by how strongly the researchers Kacper Sokol 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• FACE: Feasible and Actionable Counterfactual Explanations (2019) — 478 citations
• Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety (2019) — 94 citations
• Explainability fact sheets: a framework for systematic assessment of explainable approaches (2019) — 361 citations
• Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant (2018) — 90 citations