Benjamin Fish

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 18 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it
• Safety, Robustness & Interpretability — 8 citations into it
• Optimization & Training — 7 citations into it
• Safety Research — 7 citations into it; #1,223 of 5,829 by trust here, #1,624 by citations
• AI Ethics, Fairness & Society — 5 citations into it; #129 of 494 by trust here, #152 by citations

Whom Benjamin Fish trusts overall

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

1. Toon Calders
2. Faisal Kamiran
3. Toshihiro Kamishima
4. Richard S. Zemel
5. Robert E. Schapire
6. Shai Shalev-Shwartz
7. Shotaro Akaho
8. Yoav Freund
9. Cynthia Dwork
10. Ledell Yu Wu

Who trusts Benjamin Fish overall

1. Luca Oneto
2. Úrsula Hébert-Johnson
3. Silvia Chiappa
4. Emmanouil Krasanakis
5. Sina Baharlouei
6. Sina Aghaei
7. Sara Hajian
8. Jiachun Liao
9. Marius Miron
10. Matthew Joseph

Papers from Benjamin Fish's perspective

Ordered by how strongly the researchers Benjamin Fish 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• When not to design, build, or deploy (2020) — 40 citations
• A Confidence-Based Approach for Balancing Fairness and Accuracy (2016) — 265 citations
• Fair Boosting : a Case Study (2015)