Mary Phuong
Artificial Intelligence · 2019
A researcher in the Science corpus: 2 papers; trusts 156 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 43 citations into it; #7,893 of 35,345 by trust here, #15,236 by citations
- Computer Vision and Pattern Recognition — 30 citations into it; #7,214 of 21,575 by trust here, #17,258 by citations
- Efficiency & Compression — 19 citations into it; #527 of 1,264 by trust here, #925 by citations
- Model Architectures — 12 citations into it
- Optimization & Training — 8 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
- Signal Processing — 8 citations into it
Whom Mary Phuong trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Paul A. Viola
- Jimmy Ba
- Yann LeCun
- Diederik P. Kingma
- Jia Deng
- Ilya Sutskever
Who trusts Mary Phuong overall
- Jianping Gou
- Lin Wang
- Guangda Ji
- Anbang Yao
- Eden Belouadah
- Yoshitomo Matsubara
- Baosheng Yu
- Ji Xin
- Aditya Krishna Menon
- Beier Zhu
Papers from Mary Phuong's perspective
Ordered by how strongly the researchers Mary Phuong 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
- 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
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Towards Understanding Knowledge Distillation (2019) — 400 citations
- Distillation-Based Training for Multi-Exit Architectures (2019) — 211 citations