Nathan Hilliard

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 41 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Model Architectures — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Nathan Hilliard trusts overall

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

1. Yoshua Bengio
2. Gregory R. Koch
3. Oriol Vinyals
4. Kaiming He
5. Yann LeCun
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Ilya Sutskever
10. Adam Santoro

Who trusts Nathan Hilliard overall

1. Jun Shu
2. Debasmit Das
3. Alex Hernandez-Garcia
4. Liang Song
5. Hung-Yu Tseng
6. K. Li
7. Limeng Qiao
8. Bin Liu
9. Jie Hong
10. Zongben Xu

Papers from Nathan Hilliard's perspective

Ordered by how strongly the researchers Nathan Hilliard 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. 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. 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

• Few-Shot Learning with Metric-Agnostic Conditional Embeddings (2018) — 165 citations
• Dynamic Input Structure and Network Assembly for Few-Shot Learning (2017) — 5 citations