Rudra P. K. Poudel
Computer Vision and Pattern Recognition · 2016–2019
A researcher in the Science corpus: 2 papers; trusts 290 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 40 citations into it; #13,635 of 21,575 by trust here, #14,478 by citations
- Artificial Intelligence — 20 citations into it; #22,036 of 35,345 by trust here, #21,878 by citations
- Efficiency & Compression — 9 citations into it
- Model Architectures — 7 citations into it
Whom Rudra P. K. Poudel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Karen Simonyan
- Geoffrey E. Hinton
- Alex Krizhevsky
- Sergey Ioffe
- Christian Szegedy
- Yoshua Bengio
- Andrew Zisserman
- Ilya Sutskever
Who trusts Rudra P. K. Poudel overall
- Sangyong Park
- Tiago Cortinhal
- Huihui Pan
- Albert Y. Kim
- Huayao Liu
- Jonas Frey
- Libo Wang
- Arvid Lundervold
- Yong Seok Heo
- Yuanduo Hong
Papers from Rudra P. K. Poudel's perspective
Ordered by how strongly the researchers Rudra P. K. Poudel 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Fast-SCNN: Fast Semantic Segmentation Network (2019) — 692 citations
- Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker (2016) — 828 citations