Forough Poursabzi-Sangdeh
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 47 citations into it; #21,309 of 35,345 by trust here, #10,065 by citations
- Safety, Robustness & Interpretability — 28 citations into it; #1,395 of 2,251 by trust here, #889 by citations
- Sociology and Political Science — 14 citations into it
Whom Forough Poursabzi-Sangdeh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Himabindu Lakkaraju
- Alexandra Chouldechova
- J. Logg
- R. Caruana
- Isaac Lage
- Emilee J. Rader
- Harsha Nori
- Marco Tulio Ribeiro
- Berkeley J. Dietvorst
- Cynthia Rudin
Who trusts Forough Poursabzi-Sangdeh overall
- Q. Liao
- Giulia Vilone
- Vivian Lai
- Xinru Wang
- Samuel Carton
- Gagan Bansal
- Yunfeng Zhang
- Ben Green
- Ming Yin
- Zana Buçinca
Papers from Forough Poursabzi-Sangdeh's perspective
Ordered by how strongly the researchers Forough Poursabzi-Sangdeh 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)
- The mythos of model interpretability
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Et al
Their papers
- Manipulating and Measuring Model Interpretability (2018) — 848 citations
- A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition (2020) — 3 citations