Daniel Quang

Molecular Biology · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 84 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 43 citations into it; #1,003 of 17,773 by trust here, #5,864 by citations
• Artificial Intelligence — 18 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Genetics — 8 citations into it; #2,519 of 6,854 by trust here, #3,011 by citations
• Optimization & Training — 8 citations into it
• Computational Biology & Bioinformatics — 5 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Signal Processing — 5 citations into it

Whom Daniel Quang trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Jian Zhou
5. Nitish Srivastava
6. Yoshua Bengio
7. Ilya Sutskever
8. Alex Krizhevsky
9. Karen Simonyan
10. J. Urgen Schmidhuber

Who trusts Daniel Quang overall

1. Raquel Dias
2. Xiaoyong Pan
3. Xiaohui S. Xie
4. Chensi Cao
5. Ali Torkamani
6. Jack Lanchantin
7. Shumin Li
8. Steffen Wiewel
9. Divyanshi Srivastava
10. Alexandr A Kalinin

Papers from Daniel Quang's perspective

Ordered by how strongly the researchers Daniel Quang 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. Going deeper with convolutions
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

• DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences (2015) — 788 citations
• FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (2017) — 172 citations
• DANN: a deep learning approach for annotating the pathogenicity of genetic variants (2015) — 972 citations