Qianli Liao

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 9 papers; trusts 305 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 89 citations into it; #8,603 of 21,575 by trust here, #9,187 by citations
• Cognitive Neuroscience — 69 citations into it; #3,071 of 10,852 by trust here, #3,802 by citations
• Artificial Intelligence — 65 citations into it; #18,881 of 35,345 by trust here, #7,858 by citations
• Model Architectures — 36 citations into it; #1,241 of 2,278 by trust here, #1,433 by citations
• Optimization & Training — 30 citations into it
• Scaling & Learning Theory — 27 citations into it
• Neuroscience — 26 citations into it
• Representation & Self-Supervised Learning — 26 citations into it

Whom Qianli Liao trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Tomaso A. Poggio
5. Andrea Vedaldi
6. Yann LeCun
7. Yoshua Bengio
8. Kaiming He
9. Maximilian Riesenhuber
10. Ilya Sutskever

Who trusts Qianli Liao overall

1. Dmitry Yarotsky
2. Elliot Meyerson
3. Jordan Guerguiev
4. Tomaso A. Poggio
5. Trang Pham
6. Mengye Ren
7. Timothy P. Lillicrap
8. Qiushan Guo
9. Hrushikesh Narhar Mhaskar
10. Michael Figurnov

Papers from Qianli Liao's perspective

Ordered by how strongly the researchers Qianli Liao 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex (2016) — 268 citations
• Theory of Deep Learning III: explaining the non-overfitting puzzle (2017) — 138 citations
• How Important Is Weight Symmetry in Backpropagation? (2015) — 195 citations
• Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review (2016) — 657 citations
• Learning Functions: When Is Deep Better Than Shallow (2016) — 150 citations
• The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex (2014) — 35 citations
• Theory II: Landscape of the Empirical Risk in Deep Learning (2017) — 81 citations
• View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation (2016) — 57 citations
• Learning Real and Boolean Functions: When Is Deep Better Than Shallow (2016) — 61 citations