James Higgins

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 7 citations into it; #23,823 of 35,345 by trust here, #30,347 by citations
• Model Architectures — 7 citations into it

Whom James Higgins trusts overall

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

1. Paul J. Werbos
2. Geoffrey E. Hinton

Who trusts James Higgins overall

1. Andrés Camero

Papers from James Higgins's perspective

Ordered by how strongly the researchers James Higgins trusts endorse each paper. Same corpus, a different order for every researcher.

1. Dropout: a simple way to prevent neural networks from overfitting
2. Recurrent Neural Network Regularization
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. On the difficulty of training recurrent neural networks
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Using ant colony optimization to optimize long short-term memory recurrent neural networks (2018) — 30 citations