Mike Huisman

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 130 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Meta & Continual Learning — 17 citations into it
• Reinforcement Learning — 13 citations into it
• Optimization & Training — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Mike Huisman trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Chelsea Finn
4. J. Urgen Schmidhuber
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. Sergey Levine
9. Kaiming He
10. Yann LeCun

Papers from Mike Huisman's perspective

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

Their papers

• A survey of deep meta-learning (2020) — 418 citations