Narayanan Sundaram
Computer Vision and Pattern Recognition · 2008–2016
A researcher in the Science corpus: 7 papers; trusts 148 researchers and is trusted by 586 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 51 citations into it; #1,777 of 21,575 by trust here, #6,326 by citations
- Artificial Intelligence — 31 citations into it; #9,649 of 35,345 by trust here, #5,120 by citations
- Optimization & Training — 18 citations into it; #1,806 of 2,601 by trust here, #575 by citations
- Computational Theory and Mathematics — 15 citations into it; #7,306 of 12,182 by trust here, #641 by citations
- Probabilistic & Bayesian Methods — 15 citations into it; #1,114 of 1,525 by trust here, #251 by citations
- Evaluation & Benchmarks — 6 citations into it
- Management Science and Operations Research — 6 citations into it
Whom Narayanan Sundaram trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thomas Brox
- Jitendra Malik
- Jianbo Shi
- Geoffrey E. Hinton
- Yoshua Bengio
- Christopher Zach
- Radford M. Neal
- Peter Sand
- Alex Krizhevsky
- Yann LeCun
Who trusts Narayanan Sundaram overall
- Thomas Brox
- Heng Wang
- Pengpeng Liu
- Michael J. Black
- Jitendra Malik
- Cordelia Schmid
- Fanyi Xiao
- Shuran Song
- Jun Xie
- Anurag Ranjan
Papers from Narayanan Sundaram's perspective
Ordered by how strongly the researchers Narayanan Sundaram 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
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Dense Point Trajectories by GPU-Accelerated Large Displacement Optical Flow (2010) — 526 citations
- Scalable Bayesian Optimization Using Deep Neural Networks (2015) — 1,144 citations
- Efficient, high-quality image contour detection (2009) — 143 citations
- Fast support vector machine training and classification on graphics processors (2008) — 415 citations
- Graphicionado: A high-performance and energy-efficient accelerator for graph analytics (2016) — 351 citations
- GraphMat: High performance graph analytics made productive (2015) — 344 citations
- Navigating the maze of graph analytics frameworks using massive graph datasets (2014) — 202 citations