Eitan Richardson
Computer Vision and Pattern Recognition · 2018
A researcher in the Science corpus: 1 paper; trusts 255 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 31 citations into it; #5,719 of 21,575 by trust here, #14,478 by citations
- Generative Models — 30 citations into it; #535 of 1,956 by trust here, #1,531 by citations
- Artificial Intelligence — 13 citations into it; #7,914 of 35,345 by trust here, #21,878 by citations
- Probabilistic & Bayesian Methods — 7 citations into it; #548 of 1,525 by trust here, #1,031 by citations
- Representation & Self-Supervised Learning — 5 citations into it
Whom Eitan Richardson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yann LeCun
- Yoshua Bengio
- Aäron van den Oord
- Michael E. Tipping
- Kaiming He
- Sanjeev Arora
- Ilya Sutskever
Who trusts Eitan Richardson overall
- Ali Borji
- Alexander Rainer Tassilo Gepperth
- Hsin-Ying Lee
- Casey Meehan
- Benedikt Pfülb
- Laurent Dinh
- Shuyang Gu
- Lucy Chai
- Qi Mao
- Rongjie Huang
Papers from Eitan Richardson's perspective
Ordered by how strongly the researchers Eitan Richardson 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- On GANs and GMMs (2018) — 165 citations