Bo Xie

Artificial Intelligence · with Song · 2013–2017

A researcher in the Science corpus: 6 papers; trusts 40 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #8,206 of 35,345 by trust here, #8,893 by citations
• Statistical Learning Theory — 34 citations into it; #362 of 1,038 by trust here, #249 by citations
• Optimization & Training — 26 citations into it; #1,975 of 2,601 by trust here, #1,468 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Computational Mechanics — 17 citations into it; #4,566 of 10,001 by trust here, #2,942 by citations
• Statistics and Probability — 15 citations into it
• Model Architectures — 12 citations into it

Whom Bo Xie trusts overall

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

1. Ali Rahimi
2. Andrew R. Barron
3. B. Recht
4. Alex Smola
5. Bernhard Schölkopf
6. Geoffrey E. Hinton
7. Quoc V. Le
8. Yann LeCun
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Bo Xie overall

1. Yuanzhi Li
2. Zeyuan Allen-Zhu
3. Bo Dai
4. Alberto Bietti
5. Simon Shaolei Du
6. Daniel Soudry
7. Jiaoyang Huang
8. Ronen Basri
9. Guodong Zhang
10. Yifan Wu

Papers from Bo Xie's perspective

Ordered by how strongly the researchers Bo Xie 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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

• Scalable Kernel Methods via Doubly Stochastic Gradients (2014) — 238 citations
• Diverse Neural Network Learns True Target Functions (2016) — 147 citations
• Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients (2015) — 45 citations
• On the Complexity of Learning Neural Networks (2017) — 63 citations
• Diversity Leads to Generalization in Neural Networks (2016) — 36 citations
• Nonparametric Estimation of Multi-View Latent Variable Models (2013) — 46 citations