Jiamin Ren
Computer Vision and Pattern Recognition · 2018–2019
A researcher in the Science corpus: 2 papers; trusts 530 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 72 citations into it; #9,011 of 21,575 by trust here, #11,950 by citations
- Artificial Intelligence — 52 citations into it; #15,759 of 35,345 by trust here, #17,776 by citations
- Model Architectures — 26 citations into it
- Optimization & Training — 22 citations into it; #1,819 of 2,601 by trust here, #1,804 by citations
- Evaluation & Benchmarks — 15 citations into it
- Efficiency & Compression — 10 citations into it
Whom Jiamin Ren trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Karen Simonyan
- Ross B. Girshick
- Sergey Ioffe
- Diederik P. Kingma
- Andrew Zisserman
- Shaoqing Ren
- Yoshua Bengio
Who trusts Jiamin Ren overall
- Lei Huang
- Boyi Li
- Seonguk Seo
- Quang Pham
- Abdul Jabbar
- Hanxiao Liu
- Wenqi Shao
- Siyuan Qiao
- Xin Jin
- Po-Wei Wu
Papers from Jiamin Ren's perspective
Ordered by how strongly the researchers Jiamin Ren 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
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Differentiable Learning-to-Normalize via Switchable Normalization (2018) — 193 citations
- SSN: Learning Sparse Switchable Normalization via SparsestMax (2019) — 57 citations