Dane Taylor

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it
• Computer Vision and Pattern Recognition — 26 citations into it
• Model Architectures — 24 citations into it
• Statistical and Nonlinear Physics — 20 citations into it
• Optimization & Training — 11 citations into it

Whom Dane Taylor trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Yoshua Bengio
7. Eldad Haber
8. Richard S. Sutton
9. W. E
10. X. Zhang

Papers from Dane Taylor's perspective

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

Their papers

• Continuous-in-Depth Neural Networks (2020) — 56 citations
• Inferring causal molecular networks: empirical assessment through a community-based effort (2016) — 222 citations