Y. Nakai

Nuclear and High Energy Physics · with Farina · 2018

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Nuclear and High Energy Physics — 13 citations into it

Whom Y. Nakai trusts overall

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

1. Matteo Cacciari

Papers from Y. Nakai's perspective

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

1. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
2. Jet-images — deep learning edition
3. FastJet user manual
4. Deep Learning and Its Application to LHC Physics (Annual Review of Nuclear and Particle Science)
5. Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. ImageNet classification with deep convolutional neural networks
8. Long Short-Term Memory
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. Gradient-based learning applied to document recognition

Their papers

• Searching for new physics with deep autoencoders (2018) — 214 citations