E. Metodiev
Nuclear and High Energy Physics · 2016–2018
A researcher in the Science corpus: 4 papers; trusts 87 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Nuclear and High Energy Physics — 136 citations into it; #1,378 of 6,161 by trust here, #3,184 by citations
- Artificial Intelligence — 57 citations into it; #15,670 of 35,345 by trust here, #21,878 by citations
- Computer Vision and Pattern Recognition — 31 citations into it; #10,073 of 21,575 by trust here, #14,478 by citations
- AI for Physical Sciences — 21 citations into it
- Model Architectures — 12 citations into it
- Computer Networks and Communications — 8 citations into it
- Optimization & Training — 7 citations into it
- Electrical and Electronic Engineering — 5 citations into it
Whom E. Metodiev trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Matteo Cacciari
- Jesse Thaler
- Diederik P. Kingma
- A. Larkoski
- G. Salam
- Kaiming He
- Geoffrey E. Hinton
- Jimmy Ba
- G. Soyez
- Jason Gallicchio
Who trusts E. Metodiev overall
- Patrick T. Komiske
- A. Larkoski
- S. Marzani
- Anders Andreassen
- Jesse Thaler
- Frédéric A. Dreyer
- Benjamin Nachman
- Giuseppe Carleo
- G. Soyez
- I. Moult
Papers from E. Metodiev's perspective
Ordered by how strongly the researchers E. Metodiev trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Energy flow networks: deep sets for particle jets (2018) — 320 citations
- Classification without labels: learning from mixed samples in high energy physics (2017) — 234 citations
- Energy flow polynomials: a complete linear basis for jet substructure (2017) — 158 citations
- Deep learning in color: towards automated quark/gluon jet discrimination (2016) — 279 citations