Michelle Guo

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 152 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #18,518 of 35,345 by trust here, #15,236 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Model Architectures — 19 citations into it
• Signal Processing — 18 citations into it; #4,208 of 7,572 by trust here, #5,086 by citations
• Efficiency & Compression — 9 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Michelle Guo trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ross B. Girshick
6. Diederik P. Kingma
7. R. Caruana
8. Yann LeCun
9. Ilya Sutskever
10. Jimmy Ba

Who trusts Michelle Guo overall

1. Michael Crawshaw
2. Christopher Fifty
3. Simon Vandenhende
4. Kevis-Kokitsi Maninis
5. Shikun Liu
6. Kemal Oksuz
7. Mark Anderson
8. Ehsan Amid
9. R. Takashima
10. Ganesh Sistu

Papers from Michelle Guo's perspective

Ordered by how strongly the researchers Michelle Guo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Dynamic Task Prioritization for Multitask Learning (2018) — 457 citations
• Knowledge distillation for small-footprint highway networks (2016) — 78 citations