Asa Ben-Hur

Molecular Biology · 2001–2019

A researcher in the Science corpus: 10 papers; trusts 48 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 97 citations into it; #732 of 17,773 by trust here, #3,066 by citations
• Artificial Intelligence — 34 citations into it; #8,353 of 35,345 by trust here, #12,580 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Computational Biology & Bioinformatics — 20 citations into it
• Statistical Learning Theory — 19 citations into it
• Graph Learning — 12 citations into it
• Evaluation & Benchmarks — 10 citations into it; #2,175 of 3,147 by trust here, #2,062 by citations
• Optimization & Training — 10 citations into it

Whom Asa Ben-Hur trusts overall

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

1. Radford M. Neal
2. Bernhard Schölkopf
3. Corinna Cortes
4. Vladimir Naumovich Vapnik
5. John Shawe-Taylor
6. Nello Cristianini
7. Sara Mostafavi
8. Yann LeCun
9. Thorsten Joachims
10. Ioannis Tsochantaridis

Who trusts Asa Ben-Hur overall

1. Marina Meila
2. Ulrike von Luxburg
3. Ingo Steinwart
4. Deepti Ghadiyaram
5. Mehmet Gönen
6. Benjamin A. Shoemaker
7. Francesco Camastra
8. Ludmila I. Kuncheva
9. Céline Brouard
10. Carlo Vittorio Cannistraci

Papers from Asa Ben-Hur's perspective

Ordered by how strongly the researchers Asa Ben-Hur 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• A user's guide to support vector machines. (2010) — 871 citations
• A large-scale evaluation of computational protein function prediction (2013) — 1,002 citations
• Multi-view prediction of protein function (2011) — 11 citations
• Kernel methods for predicting protein-protein interactions (2005) — 638 citations
• A Stability Based Method for Discovering Structure in Clustered Data (2001) — 679 citations
• Support Vector Machines and Kernels for Computational Biology (2008) — 771 citations
• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
• Protein Interface Prediction using Graph Convolutional Networks (2017) — 869 citations
• Result Analysis of the NIPS 2003 Feature Selection Challenge (2004) — 724 citations
• Support Vector Clustering (2002) — 1,579 citations