Jun Huan

Molecular Biology · with Li · 2003–2017

A researcher in the Science corpus: 5 papers; trusts 23 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 32 citations into it
• Artificial Intelligence — 22 citations into it; #11,560 of 35,345 by trust here, #22,463 by citations
• Computational Theory and Mathematics — 20 citations into it
• Information Systems — 20 citations into it; #1,862 of 8,771 by trust here, #4,188 by citations
• Signal Processing — 11 citations into it; #2,063 of 7,572 by trust here, #3,673 by citations
• Computer Networks and Communications — 10 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it; #1,351 of 3,145 by trust here, #2,488 by citations

Whom Jun Huan trusts overall

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

1. Xifeng Yan
2. Mohammed J. Zaki
3. Michihiro Kuramochi
4. Jiawei Han
5. Akihiro Inokuchi
6. George Karypis
7. Karam Gouda
8. Kamal Nigam
9. Takashi Washio
10. Douglas Burdick

Who trusts Jun Huan overall

1. Michihiro Kuramochi
2. Mohammed J. Zaki
3. Mukund Deshpande
4. Jiawei Han
5. Chuntao Jiang
6. Siegfried Nijssen
7. George Karypis
8. Hong Cheng
9. François Rousseau
10. Yu Zhang

Papers from Jun Huan's perspective

Ordered by how strongly the researchers Jun Huan 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data (2017) — 271 citations
• The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models (2010) — 549 citations
• Inductive multi-task learning with multiple view data (2012) — 124 citations
• SPIN: mining maximal frequent subgraphs from graph databases (2004) — 381 citations
• Efficient mining of frequent subgraphs in the presence of isomorphism (2003) — 730 citations