Zhiyun Lu

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 4 papers; trusts 79 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #26,236 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 30 citations into it
• Signal Processing — 26 citations into it; #5,534 of 7,572 by trust here, #4,560 by citations
• Optimization & Training — 18 citations into it
• Model Architectures — 16 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Speech & Audio — 14 citations into it
• Representation & Self-Supervised Learning — 11 citations into it

Whom Zhiyun Lu trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. Diederik P. Kingma
6. Radford M. Neal
7. Sepp Hochreiter
8. Ilya Sutskever
9. J. Urgen Schmidhuber
10. Alex Graves

Who trusts Zhiyun Lu overall

1. Dong Yu
2. Jinyu Li
3. Jörn-Henrik Jacobsen
4. Seunghak Yu
5. Tamir Hazan
6. Aditya Kusupati
7. Felix Stahlberg
8. Rodolphe Jenatton
9. Junier B. Oliva
10. Edouard Oyallon

Papers from Zhiyun Lu's perspective

Ordered by how strongly the researchers Zhiyun Lu 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning compact recurrent neural networks (2016) — 88 citations
• Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and Mean-Field Approximation (2020) — 21 citations
• Improving Streaming Automatic Speech Recognition with Non-Streaming Model Distillation on Unsupervised Data (2020) — 26 citations
• How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets (2014) — 66 citations