Jinbo Bi

Artificial Intelligence · 2003–2021

A researcher in the Science corpus: 12 papers; trusts 277 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #11,711 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 88 citations into it; #4,872 of 21,575 by trust here, #7,421 by citations
• Representation & Self-Supervised Learning — 34 citations into it; #986 of 3,145 by trust here, #1,811 by citations
• Optimization & Training — 29 citations into it
• Graph Learning — 25 citations into it
• Computational Mechanics — 20 citations into it
• Statistical Learning Theory — 18 citations into it
• Molecular Biology — 17 citations into it

Whom Jinbo Bi trusts overall

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

1. Leo Breiman
2. Vladimir Naumovich Vapni
3. Geoffrey E. Hinton
4. James C. Bezdek
5. Yuhai Wu
6. Yoshua Bengio
7. Alex Krizhevsky
8. Michael I. Jordan
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Jinbo Bi overall

1. Paulo Cortez
2. Zhi-Hua Zhou
3. Manik Varma
4. Veronika Cheplygina
5. James R. Foulds
6. Paula Branco
7. Dan Zhang
8. Yu Zhang
9. Isabelle M Guyon
10. Emilio Carrizosa

Papers from Jinbo Bi's perspective

Ordered by how strongly the researchers Jinbo Bi trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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

• MILES: Multiple-Instance Learning via Embedded Instance Selection (2006) — 786 citations
• Bayesian multiple instance learning: automatic feature selection and inductive transfer (2008) — 160 citations
• Column-generation boosting methods for mixture of kernels (2004) — 96 citations
• Active learning via transductive experimental design (2006) — 388 citations
• Edge Attention-based Multi-Relational Graph Convolutional Networks (2018) — 96 citations
• Discrete Graph Structure Learning for Forecasting Multiple Time Series (2021) — 377 citations
• Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data (2020) — 29 citations
• A Survey on Multi-View Clustering (2017) — 344 citations
• On Multiplicative Multitask Feature Learning (2014) — 52 citations
• An Improved Multi-task Learning Approach with Applications in Medical Diagnosis (2008) — 121 citations
• Regression Error Characteristic Curves (2003) — 304 citations
• Dimensionality Reduction via Sparse Support Vector Machines (2003) — 492 citations