Olivier J. H'enaff
Computer Vision and Pattern Recognition · 2015–2021
A researcher in the Science corpus: 7 papers; trusts 1,085 researchers and is trusted by 592 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 325 citations into it; #1,787 of 21,575 by trust here, #2,130 by citations
- Artificial Intelligence — 255 citations into it; #4,632 of 35,345 by trust here, #2,346 by citations
- Representation & Self-Supervised Learning — 185 citations into it; #1,050 of 3,145 by trust here, #456 by citations
- Model Architectures — 60 citations into it
- Evaluation & Benchmarks — 39 citations into it; #1,129 of 3,147 by trust here, #438 by citations
- Optimization & Training — 32 citations into it
- Generative Models — 29 citations into it
- Signal Processing — 21 citations into it
Whom Olivier J. H'enaff trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Ross B. Girshick
- Karen Simonyan
- Yoshua Bengio
- Andrew Zisserman
- Ilya Sutskever
- Yann LeCun
Who trusts Olivier J. H'enaff overall
- Richard Zhang
- Kaiming He
- Xiaohua Zhai
- Ishan Misra
- Xinlei Chen
- Yonglong Tian
- Alexander Kolesnikov
- Chengxu Zhuang
- Quoc V. Le
- Zhenda Xie
Papers from Olivier J. H'enaff's perspective
Ordered by how strongly the researchers Olivier J. H'enaff 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- 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
- Data-Efficient Image Recognition with Contrastive Predictive Coding (2019) — 1,574 citations
- Are we done with ImageNet? (2020) — 483 citations
- Perceiver IO: A General Architecture for Structured Inputs & Outputs (2021) — 871 citations
- Divide and Contrast: Self-supervised Learning from Uncurated Data (2021) — 113 citations
- Perceptual straightening of natural videos (2019) — 109 citations
- Efficient Visual Pretraining with Contrastive Detection (2021) — 190 citations
- Geodesics of learned representations (2015) — 55 citations