Bin Gu

Artificial Intelligence · IEEE Transactions on Neural Networks and Learning Systems · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 122 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #15,352 of 35,345 by trust here, #13,760 by citations
• Optimization & Training — 57 citations into it; #1,824 of 2,601 by trust here, #2,428 by citations
• Computer Vision and Pattern Recognition — 37 citations into it; #8,795 of 21,575 by trust here, #8,205 by citations
• Model Architectures — 19 citations into it
• Computational Mechanics — 13 citations into it
• Management Science and Operations Research — 11 citations into it
• Electrical and Electronic Engineering — 8 citations into it
• Statistical Learning Theory — 7 citations into it

Whom Bin Gu trusts overall

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

1. Yuhai Wu
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Chih-Jen Lin
6. Yoshua Bengio
7. Kaiming He
8. Yann LeCun
9. Christopher K. I. Williams
10. L. Eon Bottou

Who trusts Bin Gu overall

1. Wu Deng
2. Lingheng Meng
3. Roozbeh Jafari
4. Eugene Belilovsky
5. Chunyong Yin
6. Pedro Antonio Gutiérrez
7. Kwok Tai Chui
8. Huimin Zhao
9. Alexander Rainer Tassilo Gepperth
10. Hui Wang

Papers from Bin Gu's perspective

Ordered by how strongly the researchers Bin Gu 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Decoupled Parallel Backpropagation with Convergence Guarantee (2018) — 107 citations
• Training Neural Networks Using Features Replay (2018) — 78 citations
• Incremental Support Vector Learning for Ordinal Regression (2015) — 712 citations
• Structural Minimax Probability Machine (2017) — 348 citations
• Faster On-Device Training Using New Federated Momentum Algorithm (2020) — 58 citations
• A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification (2017) — 470 citations
• Incremental learning for ν-Support Vector Regression (2015) — 439 citations