Fred Hohman

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 129 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #32,324 of 35,345 by trust here, #13,544 by citations
• Computer Vision and Pattern Recognition — 94 citations into it; #19,038 of 21,575 by trust here, #10,154 by citations
• Safety, Robustness & Interpretability — 73 citations into it
• Biophysics — 14 citations into it; #993 of 1,196 by trust here, #754 by citations
• Human-Computer Interaction — 11 citations into it
• Safety Research — 9 citations into it
• Information Systems and Management — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Fred Hohman trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Christian Szegedy
6. Ian J. Goodfellow
7. Yann LeCun
8. Diederik P. Kingma
9. Matthew D. Zeiler
10. Kaiming He

Who trusts Fred Hohman overall

1. S. M. Mousavi
2. Xinru Wang
3. Sina Mohseni
4. Han Liu
5. James Wexler
6. Sebastian Gehrmann
7. Ashraf Abdul
8. Yannick Roy
9. Gregory C. Beroza
10. Vivian Lai

Papers from Fred Hohman's perspective

Ordered by how strongly the researchers Fred Hohman 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models (2019) — 281 citations
• Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression (2017) — 353 citations
• Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers (2018) — 606 citations
• CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization (2020) — 318 citations