Jing Gao

Artificial Intelligence · Knowledge Discovery and Data Mining · 2008–2020

A researcher in the Science corpus: 16 papers; trusts 196 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 203 citations into it; #8,204 of 35,345 by trust here, #4,393 by citations
• Statistics and Probability — 90 citations into it
• Computer Vision and Pattern Recognition — 54 citations into it; #11,982 of 21,575 by trust here, #11,721 by citations
• Information Systems — 44 citations into it; #8,382 of 8,771 by trust here, #2,821 by citations
• Causal Inference & Discovery — 43 citations into it
• Representation & Self-Supervised Learning — 41 citations into it
• Computer Networks and Communications — 35 citations into it
• Signal Processing — 35 citations into it; #7,064 of 7,572 by trust here, #2,400 by citations

Whom Jing Gao trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Thorsten Joachims
4. Donald B. Rubin
5. Alex Graves
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Bernhard Schölkopf
9. Yann LeCun
10. Christopher K. I. Williams

Who trusts Jing Gao overall

1. Ming Shao
2. Yan Yang
3. Gabriella Csurka
4. Hao Wang
5. Changqing Zhang
6. Qiang Yang
7. Sinno Jialin Pan
8. Rita Chattopadhyay
9. Quanquan Gu
10. Nimrod Rappoport

Papers from Jing Gao's perspective

Ordered by how strongly the researchers Jing Gao 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. 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

• Knowledge transfer via multiple model local structure mapping (2008) — 349 citations
• Multi-View Clustering via Joint Nonnegative Matrix Factorization (2013) — 1,014 citations
• Graph Regularized Transductive Classification on Heterogeneous Information Networks (2010) — 261 citations
• On handling negative transfer and imbalanced distributions in multiple source transfer learning (2014) — 131 citations
• LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data (2014) — 29 citations
• EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (2018) — 1,234 citations
• Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks (2017) — 623 citations
• Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation (2014) — 507 citations
• A Survey on Causal Inference (2020) — 690 citations
• A deep learning approach for detecting traffic accidents from social media data (2018) — 318 citations
• Representation Learning for Treatment Effect Estimation from Observational Data (2018) — 348 citations
• A Survey on Truth Discovery (2015) — 449 citations
• Outlier Detection for Temporal Data: A Survey (2014) — 1,031 citations
• A Deep Learning Approach to Link Prediction in Dynamic Networks (2014) — 163 citations
• A Confidence-Aware Approach for Truth Discovery on Long-Tail Data (2014) — 346 citations
• Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints (2011) — 415 citations