Liam Hodgkinson

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it
• Statistical and Nonlinear Physics — 13 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Statistics and Probability — 8 citations into it
• Generative Models — 5 citations into it

Whom Liam Hodgkinson trusts overall

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

1. A. Davie
2. Taye Samuel Faniran
3. Peter K. Friz
4. Mark A. Girolami
5. Qiang Liu
6. Belinda Tzen
7. John C. Duchi
8. Aubrey Hurn
9. T. Chen
10. Alexandros Beskos

Papers from Liam Hodgkinson's perspective

Ordered by how strongly the researchers Liam Hodgkinson 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. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
10. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

Their papers

• Stochastic Normalizing Flows (2020)