Baicen Xiao

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 126 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #27,037 of 35,345 by trust here, #23,731 by citations
• Safety, Robustness & Interpretability — 45 citations into it; #1,743 of 2,251 by trust here, #2,041 by citations
• Computer Vision and Pattern Recognition — 43 citations into it; #16,587 of 21,575 by trust here, #15,955 by citations
• Reinforcement Learning — 31 citations into it
• Model Architectures — 8 citations into it
• Computer Networks and Communications — 7 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Media Technology — 7 citations into it

Whom Baicen Xiao trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yann LeCun
4. Richard S. Sutton
5. Karen Simonyan
6. Yoshua Bengio
7. Ilya Sutskever
8. Diederik P. Kingma
9. Christian Szegedy
10. Kaiming He

Who trusts Baicen Xiao overall

1. Dan Hendrycks
2. Thomas G. Dietterich

Papers from Baicen Xiao's perspective

Ordered by how strongly the researchers Baicen Xiao 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Google's Cloud Vision API is Not Robust to Noise (2017) — 133 citations
• Assessing Shape Bias Property of Convolutional Neural Networks (2018) — 40 citations
• FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback (2020) — 28 citations
• Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API (2017) — 25 citations