J. Kai

Artificial Intelligence · 2017

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

Subfields

• Artificial Intelligence — 7 citations into it

Whom J. Kai trusts overall

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

1. Yoshua Bengio

Papers from J. Kai's perspective

Ordered by how strongly the researchers J. Kai 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. Gradient-based learning applied to document recognition
4. Practical Recommendations for Gradient-Based Training of Deep Architectures
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks
10. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. (Journal of the American Medical Association (JAMA))

Their papers

• Can machine-learning improve cardiovascular risk prediction using routine clinical data? (2017) — 1,188 citations