Liam H. Fowl

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 6 papers; trusts 409 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 136 citations into it; #35,100 of 35,345 by trust here, #17,110 by citations
• Computer Vision and Pattern Recognition — 91 citations into it; #21,485 of 21,575 by trust here, #15,162 by citations
• Safety, Robustness & Interpretability — 49 citations into it; #2,240 of 2,251 by trust here, #1,880 by citations
• Representation & Self-Supervised Learning — 33 citations into it
• Optimization & Training — 22 citations into it
• Generative Models — 9 citations into it
• Meta & Continual Learning — 8 citations into it
• Model Architectures — 7 citations into it

Whom Liam H. Fowl trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Kaiming He
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Oriol Vinyals
7. Yann LeCun
8. Ilya Sutskever
9. Ruslan Salakhutdinov
10. Christian Szegedy

Who trusts Liam H. Fowl overall

1. Micah Goldblum
2. Naveed Akhtar
3. Anqi Mao
4. Eric Wallace

Papers from Liam H. Fowl's perspective

Ordered by how strongly the researchers Liam H. Fowl 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks (2020) — 83 citations
• MetaPoison: Practical General-purpose Clean-label Data Poisoning (2020) — 237 citations
• Adversarially Robust Distillation (2019) — 270 citations
• Adversarially Robust Few-Shot Learning: A Meta-Learning Approach (2019) — 103 citations
• Understanding Generalization through Visualizations (2019) — 93 citations
• Robust Few-Shot Learning with Adversarially Queried Meta-Learners (2019) — 14 citations