Laurent Najman
Computer Vision and Pattern Recognition · 2007–2017
A researcher in the Science corpus: 7 papers; trusts 267 researchers and is trusted by 3,062 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 103 citations into it; #666 of 21,575 by trust here, #1,392 by citations
- Artificial Intelligence — 16 citations into it
- Aerospace Engineering — 11 citations into it
- Signal Processing — 10 citations into it
- Optimization & Training — 7 citations into it
- Computational Theory and Mathematics — 6 citations into it
- Representation & Self-Supervised Learning — 6 citations into it
- Electrical and Electronic Engineering — 5 citations into it
Whom Laurent Najman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- L. Eon Bottou
- Geoffrey E. Hinton
- Yoshua Bengio
- Yuri Boykov
- Alex Krizhevsky
- Koray Kavukcuoglu
- Leo J. Grady
- Pedro F. Felzenszwalb
- Rob Fergus
Who trusts Laurent Najman overall
- Liang-Chieh Chen
- Ross B. Girshick
- Jitendra Malik
- Xiaogang Wang
- Bharath Hariharan
- Trevor Darrell
- Yi Yang
- Pedro H. O. Pinheiro
- Wanli Ouyang
- Vijay Badrinarayanan
Papers from Laurent Najman's perspective
Ordered by how strongly the researchers Laurent Najman 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)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- 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
- Learning Hierarchical Features for Scene Labeling (2013) — 2,781 citations
- Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers (2012) — 205 citations
- Indoor Semantic Segmentation using depth information (2013) — 512 citations
- Power Watershed: A Unifying Graph-Based Optimization Framework (2011) — 331 citations
- Modern Approaches to Discrete Curvature (2017) — 48 citations
- Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle (2009) — 365 citations
- Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving (2007) — 300 citations