Luke de Oliveira

Nuclear and High Energy Physics · 2015–2017

A researcher in the Science corpus: 5 papers; trusts 53 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Nuclear and High Energy Physics — 45 citations into it; #2,920 of 6,161 by trust here, #3,136 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #12,490 of 21,575 by trust here, #9,563 by citations
• Generative Models — 18 citations into it
• Artificial Intelligence — 16 citations into it; #19,973 of 35,345 by trust here, #13,962 by citations
• AI for Physical Sciences — 14 citations into it
• Computer Networks and Communications — 9 citations into it
• Natural Language Processing — 8 citations into it
• Optimization & Training — 8 citations into it

Whom Luke de Oliveira trusts overall

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

1. Matteo Cacciari
2. Diederik P. Kingma
3. Jesse Thaler
4. Geoffrey E. Hinton
5. G. Salam
6. Ian J. Goodfellow
7. Yoshua Bengio
8. Jimmy Ba
9. Kaiming He
10. Andrew L. Maas

Who trusts Luke de Oliveira overall

1. Patrick T. Komiske
2. E. Metodiev
3. Yongjun Hong
4. Anders Andreassen
5. Benjamin Nachman
6. Anton Osokin
7. Frédéric A. Dreyer
8. Jesse Thaler
9. Michela Paganini
10. Uiwon Hwang

Papers from Luke de Oliveira's perspective

Ordered by how strongly the researchers Luke de Oliveira 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Jet-images — deep learning edition (2015) — 344 citations
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks (2017) — 359 citations
• Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training (2017)
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis (2017) — 306 citations
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters. (2017) — 252 citations