Haixu Tang

Molecular Biology · 2008–2019

A researcher in the Science corpus: 10 papers; trusts 37 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 116 citations into it; #2,876 of 17,773 by trust here, #5,601 by citations
• Artificial Intelligence — 71 citations into it; #25,537 of 35,345 by trust here, #11,192 by citations
• Genetics — 43 citations into it; #3,630 of 6,854 by trust here, #1,538 by citations
• Ecology — 35 citations into it; #3,696 of 10,717 by trust here, #9,634 by citations
• Signal Processing — 31 citations into it
• Safety, Robustness & Interpretability — 29 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it; #18,670 of 21,575 by trust here, #19,310 by citations
• Computational Biology & Bioinformatics — 11 citations into it

Whom Haixu Tang trusts overall

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

1. Sean R. Eddy
2. W. James Kent
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Yann LeCun
7. Stephen F. Altschul
8. Yoshua Bengio
9. Pavel A. Pevzner
10. Mya Breitbart

Who trusts Haixu Tang overall

1. Martin Steinegger
2. Alejandra Escobar-Zepeda
3. Hayssam Soueidan
4. John C. Wooley
5. Nicola Segata
6. Johannes Söding
7. Chengwei Luo
8. David Arndt
9. Sitao Wu
10. Yu-Wei Wu

Papers from Haixu Tang's perspective

Ordered by how strongly the researchers Haixu Tang 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. Gradient-based learning applied to document recognition
4. Learning Multiple Layers of Features from Tiny Images
5. ImageNet Large Scale Visual Recognition Challenge
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Understanding Membership Inferences on Well-Generalized Learning Models (2018) — 249 citations
• FragGeneScan: predicting genes in short and error-prone reads (2010) — 793 citations
• RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data (2011) — 411 citations
• An ORFome assembly approach to metagenomics sequences analysis. (2008) — 37 citations
• The Ecoresponsive Genome of Daphnia pulex (2011) — 1,240 citations
• Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection (2019) — 253 citations
• Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGX (2017) — 411 citations
• ISEScan: automated identification of insertion sequence elements in prokaryotic genomes (2017) — 396 citations
• Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing (2012) — 674 citations
• Learning your identity and disease from research papers: information leaks in genome wide association study (2009) — 262 citations