Mehmet Gönen
Molecular Biology · 2008–2014
A researcher in the Science corpus: 4 papers; trusts 69 researchers and is trusted by 83 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 46 citations into it; #1,327 of 17,773 by trust here, #13,563 by citations
- Artificial Intelligence — 18 citations into it; #4,003 of 35,345 by trust here, #8,706 by citations
- Computational Theory and Mathematics — 16 citations into it; #1,276 of 12,182 by trust here, #2,761 by citations
- Computer Vision and Pattern Recognition — 11 citations into it; #3,762 of 21,575 by trust here, #6,206 by citations
- Computational Mechanics — 8 citations into it
- Computational Biology & Bioinformatics — 7 citations into it
- Statistical Learning Theory — 7 citations into it; #222 of 1,038 by trust here, #326 by citations
- Optimization & Training — 6 citations into it
Whom Mehmet Gönen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Radford M. Neal
- Christopher K. I. Williams
- Minoru Kanehisa
- Matthew J. Beal
- Bernhard Schölkopf
- Yoshihiro Yamanishi
- Francis R. Bach
- Gert R. G. Lanckriet
- Temple F. Smith
- Michael I. Jordan
Who trusts Mehmet Gönen overall
- Andrew Gordon Wilson
- Yong Liu
- Hao Ding
- David Kristjanson Duvenaud
- Marinka Zitnik
- Masashi Tsubaki
- Mehreen Ali
- Xiaodong Zheng
- Hanqing Xue
- Jakramate Bootkrajang
Papers from Mehmet Gönen's perspective
Ordered by how strongly the researchers Mehmet Gönen 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Multiple Kernel Learning Algorithms (2011) — 1,988 citations
- Localized multiple kernel learning (2008) — 313 citations
- Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization (2012) — 373 citations
- A community effort to assess and improve drug sensitivity prediction algorithms (2014) — 728 citations