Srinivas C. Turaga
Molecular Biology · 2007–2017
A researcher in the Science corpus: 8 papers; trusts 192 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 268 citations into it; #13,263 of 17,773 by trust here, #3,584 by citations
- Artificial Intelligence — 208 citations into it; #34,932 of 35,345 by trust here, #5,495 by citations
- Computer Vision and Pattern Recognition — 162 citations into it; #14,556 of 21,575 by trust here, #8,409 by citations
- Computational Theory and Mathematics — 72 citations into it
- Biophysics — 57 citations into it; #525 of 1,196 by trust here, #92 by citations
- Computational Biology & Bioinformatics — 30 citations into it
- Signal Processing — 22 citations into it
- Media Technology — 20 citations into it; #3,219 of 3,621 by trust here, #2,114 by citations
Whom Srinivas C. Turaga trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- Jitendra Malik
- David R. Martin
- Charless C. Fowlkes
- Pablo Arbeláez
- Jianbo Shi
- Pedro F. Felzenszwalb
- Geoffrey E. Hinton
- Yuri Boykov
- L. Eon Bottou
Who trusts Srinivas C. Turaga overall
- Viren Jain
- Moritz Helmstaedter
- Mojtaba Seyedhosseini
- Tao Zeng
- Catalin Ionescu
- Pedro H. O. Pinheiro
- Xiaoling Hu
- Wenlu Zhang
- Jonathan Masci
- Yoshua Bengio
Papers from Srinivas C. Turaga's perspective
Ordered by how strongly the researchers Srinivas C. Turaga 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)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Supervised Learning of Image Restoration with Convolutional Networks (2007) — 253 citations
- Maximin affinity learning of image segmentation (2009) — 150 citations
- Opportunities and obstacles for deep learning in biology and medicine (2017) — 2,030 citations
- Connectomic reconstruction of the inner plexiform layer in the mouse retina (2013) — 981 citations
- Crowdsourcing the creation of image segmentation algorithms for connectomics (2015) — 371 citations
- Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit (2017) — 25 citations
- 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