Henry Gouk

Artificial Intelligence · with Cree · 2018

A researcher in the Science corpus: 2 papers; trusts 74 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #20,492 of 35,345 by trust here, #15,236 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #12,779 of 21,575 by trust here, #10,334 by citations
• Optimization & Training — 18 citations into it
• Generative Models — 8 citations into it
• Model Architectures — 8 citations into it
• Computational Mechanics — 7 citations into it
• Scaling & Learning Theory — 7 citations into it

Whom Henry Gouk trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Karen Simonyan
4. Geoffrey E. Hinton
5. Sergey Ioffe
6. Tim Salimans
7. Yoshua Bengio
8. Behnam Neyshabur
9. Kaiming He
10. Christian Szegedy

Who trusts Henry Gouk overall

1. Jens Behrmann
2. Hanie Sedghi
3. Klas Leino
4. Jeremy M. Cohen
5. Cem Anil
6. Ricky T. Q. Chen
7. Sam Bond-Taylor
8. R. Cornish
9. Florian Graf
10. Zac Cranko

Papers from Henry Gouk's perspective

Ordered by how strongly the researchers Henry Gouk 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Regularisation of neural networks by enforcing Lipschitz continuity (2018) — 626 citations
• MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes (2018) — 7 citations