Chuong B. Do
Molecular Biology · 2005–2014
A researcher in the Science corpus: 9 papers; trusts 38 researchers and is trusted by 41 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 23 citations into it; #1,189 of 17,773 by trust here, #5,452 by citations
- Artificial Intelligence — 21 citations into it; #19,985 of 35,345 by trust here, #22,463 by citations
- Computational Biology & Bioinformatics — 12 citations into it
- Optimization & Training — 11 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
- Statistical Learning Theory — 5 citations into it; #1,002 of 1,038 by trust here, #817 by citations
- Management Science and Operations Research — 5 citations into it; #6,887 of 11,946 by trust here, #9,931 by citations
- Natural Language Processing — 5 citations into it
Whom Chuong B. Do trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Thorsten Joachims
- Shai Shalev-Shwartz
- S. Sathiya Keerthi
- David John Cameron MacKay
- Anders Krogh
- Tom Minka
- Barak A. Pearlmutter
- Sam Griffiths-Jones
- Kevin P. Murphy
Who trusts Chuong B. Do overall
- Kazutaka Katoh
- Byung-Jun Yoon
- H. Brendan McMahan
- Bruce G. Marcot
- Quoc V. Le
- Fabian Pedregosa
- Zhiyong Wang
- Andreas Biegert
- R. Pressat
- Sayed Mohammad Ebrahim Sahraeian
Papers from Chuong B. Do's perspective
Ordered by how strongly the researchers Chuong B. Do trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Proximal regularization for online and batch learning (2009) — 56 citations
- Tuned Models of Peer Assessment in MOOCs (2013) — 416 citations
- ProbCons: Probabilistic consistency-based multiple sequence alignment. (2005) — 1,197 citations
- CONTRAfold: RNA secondary structure prediction without physics-based models (2006) — 612 citations
- Tighter Bounds for Structured Estimation (2008) — 73 citations
- What is the expectation maximization algorithm? (2008) — 383 citations
- Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease (2014) — 1,858 citations
- A genetic variant near olfactory receptor genes influences cilantro preference (2012) — 107 citations
- Efficient multiple hyperparameter learning for log-linear models (2007) — 120 citations