Bo Chen
Computer Vision and Pattern Recognition · with Chu · 2017–2020
A researcher in the Science corpus: 3 papers; trusts 245 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 99 citations into it; #12,584 of 21,575 by trust here, #2,494 by citations
- Artificial Intelligence — 87 citations into it; #30,630 of 35,345 by trust here, #11,072 by citations
- Efficiency & Compression — 42 citations into it; #1,053 of 1,264 by trust here, #162 by citations
- Model Architectures — 39 citations into it
- Optimization & Training — 13 citations into it
- Reinforcement Learning — 6 citations into it
Whom Bo Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Barret Zoph
- Quoc V. Le
- Karen Simonyan
- Yoshua Bengio
- Yann LeCun
- Christian Szegedy
- Diederik P. Kingma
Who trusts Bo Chen overall
Papers from Bo Chen's perspective
Ordered by how strongly the researchers Bo Chen 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
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Searching for MobileNetV3 (2019) — 9,744 citations
- MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (2017) — 362 citations
- Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS (2020) — 131 citations