Viren Jain
Computer Vision and Pattern Recognition · Nature · 2007–2020
A researcher in the Science corpus: 10 papers; trusts 319 researchers and is trusted by 775 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 108 citations into it; #1,566 of 21,575 by trust here, #4,469 by citations
- Biophysics — 28 citations into it; #715 of 1,196 by trust here, #293 by citations
- Artificial Intelligence — 24 citations into it; #34,943 of 35,345 by trust here, #15,236 by citations
- Structural Biology — 19 citations into it
- Molecular Biology — 18 citations into it; #13,264 of 17,773 by trust here, #9,136 by citations
- Media Technology — 17 citations into it
- Neuroscience — 17 citations into it
- Model Architectures — 15 citations into it
Whom Viren Jain trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- Jitendra Malik
- Charless C. Fowlkes
- David R. Martin
- Geoffrey E. Hinton
- Yuri Boykov
- Yoshua Bengio
- L. Eon Bottou
- Stuart Geman
- Pedro F. Felzenszwalb
Who trusts Viren Jain overall
- Yoshua Bengio
- Kai Zhang
- J. Urgen Schmidhuber
- Qifeng Chen
- Chunhua Shen
- Xiao-Jiao Mao
- Lei Zhang
- Wangmeng Zuo
- Ping Luo
- Chao Dong
Papers from Viren Jain's perspective
Ordered by how strongly the researchers Viren Jain trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- 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
- Natural Image Denoising with Convolutional Networks (2008) — 982 citations
- Supervised Learning of Image Restoration with Convolutional Networks (2007) — 253 citations
- Boundary Learning by Optimization with Topological Constraints (2010) — 155 citations
- Connectomic reconstruction of the inner plexiform layer in the mouse retina (2013) — 981 citations
- Superhuman Accuracy on the SNEMI3D Connectomics Challenge (2017) — 321 citations
- A connectome and analysis of the adult Drosophila central brain (2020) — 884 citations
- High-precision automated reconstruction of neurons with flood-filling networks (2017) — 380 citations
- Grand Challenges for Global Brain Sciences (2016)
- Machines that learn to segment images: a crucial technology for connectomics. (2010) — 170 citations
- Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation (2010) — 408 citations