Menaka Narayanan
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 3 papers; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 78 citations into it
- Safety, Robustness & Interpretability — 45 citations into it
- Information Systems — 20 citations into it
- Statistical Learning Theory — 10 citations into it
- Natural Language Processing — 10 citations into it
- Cognitive Science & Psychology — 7 citations into it
- Algorithms & Complexity — 6 citations into it
- Computational Theory and Mathematics — 6 citations into it
Whom Menaka Narayanan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Finale Doshi-Velez
- Been Kim
- Tania Lombrozo
- Niko Speybroeck
- Cynthia Rudin
- Himabindu Lakkaraju
- Fulton Wang
- Sameer Singh
- Viktoriya Krakovna
- Michael C. Hughes
Who trusts Menaka Narayanan overall
- Ben Green
- Isaac Lage
- Vivian Lai
- Michael Chromik
- Q. Liao
- Gagan Bansal
- Wojciech Samek
- Felix Feldmann
- Yiling Chen
- Sina Mohseni
Papers from Menaka Narayanan's perspective
Ordered by how strongly the researchers Menaka Narayanan trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018) — 262 citations
- An Evaluation of the Human-Interpretability of Explanation (2019) — 186 citations
- Human Evaluation of Models Built for Interpretability (2019) — 152 citations