Sarangapani Jagannathan
Computer Vision and Pattern Recognition · 2010–2020
A researcher in the Science corpus: 2 papers; trusts 378 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 88 citations into it
- Artificial Intelligence — 82 citations into it; #19,115 of 35,345 by trust here, #25,040 by citations
- Efficiency & Compression — 63 citations into it
- Model Architectures — 25 citations into it
- Signal Processing — 15 citations into it
- Optimization & Training — 11 citations into it
- Electrical and Electronic Engineering — 8 citations into it
- Evaluation & Benchmarks — 5 citations into it
Whom Sarangapani Jagannathan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Alex Krizhevsky
- Kaiming He
- Ilya Sutskever
- Karen Simonyan
- Diederik P. Kingma
- Christian Szegedy
- Ross B. Girshick
Who trusts Sarangapani Jagannathan overall
Papers from Sarangapani Jagannathan's perspective
Ordered by how strongly the researchers Sarangapani Jagannathan 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Output Feedback Control of a Quadrotor UAV Using Neural Networks (2010) — 552 citations
- A comprehensive survey on model compression and acceleration (2020) — 519 citations