Michelle Bao

Safety, Robustness & Interpretability · 2021

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Safety, Robustness & Interpretability — 42 citations into it
• Natural Language Processing — 11 citations into it

Whom Michelle Bao trusts overall

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

1. Sarah Singh
2. Timnit Gebru
3. Ben Green
4. Alexandra Chouldechova
5. Sorelle A. Friedler
6. Himabindu Lakkaraju
7. Jay P. Singh
8. M. Stevenson
9. B. Harcourt
10. Reuben Binns

Papers from Michelle Bao's perspective

Ordered by how strongly the researchers Michelle Bao 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. Gradient-based learning applied to document recognition
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments

Their papers

• The Values Encoded in Machine Learning Research (2021) — 401 citations
• It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks (2021) — 118 citations