Arzucan Özgür

Molecular Biology · 2005–2018

A researcher in the Science corpus: 4 papers; trusts 40 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 48 citations into it; #7,037 of 17,773 by trust here, #8,678 by citations
• Computational Theory and Mathematics — 22 citations into it; #7,322 of 12,182 by trust here, #3,562 by citations
• Artificial Intelligence — 17 citations into it; #21,972 of 35,345 by trust here, #30,347 by citations
• Natural Language Processing — 10 citations into it
• Materials Chemistry — 9 citations into it
• Computational Biology & Bioinformatics — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Computational Chemistry & Drug Discovery — 5 citations into it

Whom Arzucan Özgür trusts overall

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

1. C. Burges
2. Yiming Yang
3. Thorsten Joachims
4. Bernhard Schölkopf
5. George Forman
6. Gerard A. Salton
7. Geoffrey E. Hinton
8. Andrew McCallum
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Arzucan Özgür overall

1. Ehsan Hoseinzade
2. Yuan Luo
3. François Rousseau
4. Valerio Basile
5. Ondrej Bojar
6. Saman Haratizadeh
7. Matteo Barigozzi
8. Ozlem Uzuner
9. Emmanouil Kiagias
10. Philipp Koehn

Papers from Arzucan Özgür's perspective

Ordered by how strongly the researchers Arzucan Özgür 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. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Text Categorization with Class-Based and Corpus-Based Keyword Selection (2005) — 155 citations
• DeepDTA: deep drug–target binding affinity prediction (2018) — 1,519 citations
• Identifying gene-disease associations using centrality on a literature mined gene-interaction network (2008) — 386 citations
• Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining (2012) — 402 citations