Indra Elizabeth Kumar
Artificial Intelligence · 2020–2022
A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 66 citations into it
- Safety, Robustness & Interpretability — 53 citations into it
- Safety Research — 33 citations into it
- AI Ethics, Fairness & Society — 17 citations into it
- Evaluation & Benchmarks — 11 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
- Natural Language Processing — 8 citations into it
- Information Systems — 5 citations into it
Whom Indra Elizabeth Kumar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Scott M. Lundberg
- Andrew D. Selbst
- Suresh Venkatasubramanian
- Stan Lipovetsky
- Solon Barocas
- Marco Tulio Ribeiro
- Luke Merrick
- Su-In Lee
- Berk Ustun
- Ankur Taly
Who trusts Indra Elizabeth Kumar overall
Papers from Indra Elizabeth Kumar's perspective
Ordered by how strongly the researchers Indra Elizabeth Kumar 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)
- ImageNet Large Scale Visual Recognition Challenge
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- The Fallacy of AI Functionality (2022) — 292 citations
- Epistemic values in feature importance methods: Lessons from feminist epistemology (2021) — 46 citations
- Problems with Shapley-value-based explanations as feature importance measures (2020) — 484 citations