David K. Gifford

Molecular Biology · 2000–2019

A researcher in the Science corpus: 18 papers; trusts 62 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 170 citations into it; #2,777 of 17,773 by trust here, #485 by citations
• Artificial Intelligence — 29 citations into it; #26,643 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Natural Language Processing — 10 citations into it
• Computational Biology & Bioinformatics — 7 citations into it
• Generative Models — 7 citations into it
• Model Architectures — 5 citations into it
• Optimization & Training — 5 citations into it

Whom David K. Gifford trusts overall

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

1. Diederik P. Kingma
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Krizhevsky
6. Alex Graves
7. Tomas Mikolov
8. Jimmy Ba
9. Ruslan Salakhutdinov
10. Yann LeCun

Who trusts David K. Gifford overall

1. Antony Ian Taylor Rowstron
2. Miguel Oom Temudo de Castro
3. Duc-Anh Tran
4. Michael Hecker
5. Peter Druschel
6. Hao Wang
7. Tong Niu
8. Ilya Shmulevich
9. Florian Markowetz
10. Jennie L. Reed

Papers from David K. Gifford's perspective

Ordered by how strongly the researchers David K. Gifford 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Control of developmental regulators by Polycomb in human embryonic stem cells. (2006) — 2,442 citations
• Polycomb complexes repress developmental regulators in murine embryonic stem cells (2006) — 2,693 citations
• Core transcriptional regulatory circuitry in human embryonic stem cells. (2005) — 4,688 citations
• Genome-wide map of nucleosome acetylation and methylation in yeast. (2005) — 1,597 citations
• Transcriptional regulatory code of a eukaryotic genome (2004) — 2,390 citations
• Control of Pancreas and Liver Gene Expression by HNF Transcription Factors (2004) — 1,414 citations
• Computational discovery of gene modules and regulatory networks (2003) — 690 citations
• Serial regulation of transcriptional regulators in the yeast cell cycle. (2001) — 707 citations
• Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models (2001) — 321 citations
• Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks (2000) — 448 citations
• Discovery of non-directional and directional pioneer transcription factors by modeling DNase profile magnitude and shape (2014) — 431 citations
• Transcriptional Regulatory Networks in Saccharomyces cerevisiae (2002) — 439 citations
• Convolutional neural network architectures for predicting DNA–protein binding (2016) — 477 citations
• Antibody complementarity determining region design using high-capacity machine learning (2019) — 172 citations
• High Resolution Genome Wide Binding Event Finding and Motif Discovery Reveals Transcription Factor Spatial Binding Constraints (2012) — 304 citations
• Predictable and precise template-free CRISPR editing of pathogenic variants (2018) — 497 citations
• Sequence to Better Sequence: Continuous Revision of Combinatorial Structures (2017) — 100 citations
• Overcast: reliable multicasting with on overlay network (2000) — 1,277 citations