Jieming Zhu

Information Systems · 2017 IEEE International Conference on Web Services (ICWS) · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 69 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 49 citations into it; #4,221 of 8,771 by trust here, #4,885 by citations
• Artificial Intelligence — 47 citations into it; #30,585 of 35,345 by trust here, #27,551 by citations
• Computer Networks and Communications — 29 citations into it; #3,964 of 9,886 by trust here, #3,993 by citations
• Software — 28 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Graph Learning — 13 citations into it
• Natural Language Processing — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Jieming Zhu trusts overall

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

1. Adam J. Oliner
2. Min Du
3. Diederik P. Kingma
4. Yann LeCun
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. L. Eon Bottou
8. Wei Xu
9. Ilya Sutskever
10. Tomas Mikolov

Who trusts Jieming Zhu overall

1. Van-Hoang Le
2. Haixuan Guo
3. Hongyu Zhang
4. Shilin He
5. Shuhan Yuan
6. Pinjia He
7. Xintao Wu
8. Min Du

Papers from Jieming Zhu's perspective

Ordered by how strongly the researchers Jieming Zhu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The Supplementary Material: Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding (2020)
• UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation (2021) — 471 citations
• Tools and Benchmarks for Automated Log Parsing (2018) — 528 citations
• Drain: An Online Log Parsing Approach with Fixed Depth Tree (2017) — 961 citations
• Software Defect Prediction via Convolutional Neural Network (2017) — 406 citations
• Experience Report: System Log Analysis for Anomaly Detection (2016) — 589 citations