Nicholas Frosst

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 143 researchers and is trusted by 368 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #1,925 of 35,345 by trust here, #2,630 by citations
• Computer Vision and Pattern Recognition — 43 citations into it; #2,443 of 21,575 by trust here, #2,739 by citations
• Safety, Robustness & Interpretability — 23 citations into it
• Model Architectures — 16 citations into it; #162 of 2,278 by trust here, #295 by citations
• Optimization & Training — 16 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Generative Models — 7 citations into it
• Signal Processing — 7 citations into it

Whom Nicholas Frosst trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Ian J. Goodfellow
4. Diederik P. Kingma
5. Yoshua Bengio
6. Christian Szegedy
7. Yann LeCun
8. Michael I. Jordan
9. Kaiming He
10. Eric R. Ziegel

Who trusts Nicholas Frosst overall

1. Geoffrey E. Hinton
2. Quanshi Zhang
3. Chandan Singh
4. W. James Murdoch
5. Giulia Vilone
6. Nadia Burkart
7. Vinícius G. Costa
8. Nitish Srivastava
9. Wei Zhao
10. Ruishan Liu

Papers from Nicholas Frosst's perspective

Ordered by how strongly the researchers Nicholas Frosst trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Dynamic Routing Between Capsules (2017) — 5,162 citations
• Analyzing and Improving Representations with the Soft Nearest Neighbor Loss (2019) — 194 citations
• Distilling a Neural Network Into a Soft Decision Tree (2017) — 715 citations
• Neural Additive Models: Interpretable Machine Learning with Neural Nets (2020) — 612 citations
• Matrix capsules with EM routing (2018) — 1,142 citations