Ruth C. Fong

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 1,111 researchers and is trusted by 223 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 178 citations into it; #2,566 of 21,575 by trust here, #3,909 by citations
• Artificial Intelligence — 174 citations into it; #1,710 of 35,345 by trust here, #3,907 by citations
• Safety, Robustness & Interpretability — 81 citations into it; #111 of 2,251 by trust here, #367 by citations
• Representation & Self-Supervised Learning — 77 citations into it
• Signal Processing — 35 citations into it
• Multimodal Learning — 28 citations into it
• Biophysics — 20 citations into it
• Evaluation & Benchmarks — 17 citations into it

Whom Ruth C. Fong trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Andrea Vedaldi
5. Andrew Zisserman
6. Kaiming He
7. Ilya Sutskever
8. Christian Szegedy
9. Yoshua Bengio
10. Ross B. Girshick

Who trusts Ruth C. Fong overall

1. Wojciech Samek
2. Quanshi Zhang
3. Hila Chefer
4. Shir Gur
5. Jinjin Gu
6. Lior Wolf
7. Ian Covert
8. Marco Ancona
9. Grgoire Montavon
10. Song-Chun Zhu

Papers from Ruth C. Fong's perspective

Ordered by how strongly the researchers Ruth C. Fong 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Interpretable Explanations of Black Boxes by Meaningful Perturbation (2017) — 1,723 citations
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020) — 480 citations
• Net2Vec: Quantifying and Explaining How Concepts are Encoded by Filters in Deep Neural Networks (2018) — 295 citations
• Understanding Deep Networks via Extremal Perturbations and Smooth Masks (2019) — 494 citations
• Multi-modal Self-Supervision from Generalized Data Transformations (2020) — 196 citations