Edgar Simo-Serra
Computer Vision and Pattern Recognition · 2014–2017
A researcher in the Science corpus: 8 papers; trusts 903 researchers and is trusted by 609 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 182 citations into it; #1,770 of 21,575 by trust here, #2,295 by citations
- Artificial Intelligence — 36 citations into it
- Aerospace Engineering — 18 citations into it; #5,799 of 6,421 by trust here, #1,154 by citations
- Model Architectures — 17 citations into it
- Media Technology — 16 citations into it; #196 of 3,621 by trust here, #177 by citations
- Computational Mechanics — 15 citations into it
- Computer Graphics and Computer-Aided Design — 14 citations into it
- Generative Models — 11 citations into it; #1,397 of 1,956 by trust here, #1,435 by citations
Whom Edgar Simo-Serra trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- David G. Lowe
- Yann LeCun
- Karen Simonyan
- Kaiming He
- Ilya Sutskever
- Matthew D. Zeiler
- Ross B. Girshick
- Yoshua Bengio
Who trusts Edgar Simo-Serra overall
- Xiaodan Liang
- Filip Radenovic
- Iasonas Kokkinos
- X. Wang
- Ke Gong
- Giorgos Tolias
- Ignacio Rocco
- Abhinav Kumar Gupta
- Shuochen Su
- Albert Gordo
Papers from Edgar Simo-Serra's perspective
Ordered by how strongly the researchers Edgar Simo-Serra trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Globally and locally consistent image completion (2017) — 2,208 citations
- Discriminative Learning of Deep Convolutional Feature Point Descriptors (2015) — 817 citations
- Let there be color! (2016) — 508 citations
- Fracking Deep Convolutional Image Descriptors (2014) — 71 citations
- Neuroaesthetics in fashion: Modeling the perception of fashionability (2015) — 227 citations
- A High Performance CRF Model for Clothes Parsing (2014) — 96 citations
- Learning to simplify (2016) — 130 citations
- Geodesic Finite Mixture Models (2014) — 15 citations