Ryan M. Rifkin

Artificial Intelligence · 2001–2016

A researcher in the Science corpus: 8 papers; trusts 30 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #16,514 of 35,345 by trust here, #8,283 by citations
• Statistical Learning Theory — 21 citations into it
• Optimization & Training — 13 citations into it
• Computational Theory and Mathematics — 11 citations into it; #9,873 of 12,182 by trust here, #2,931 by citations
• Computer Vision and Pattern Recognition — 11 citations into it; #20,986 of 21,575 by trust here, #18,266 by citations
• Computational Mechanics — 9 citations into it; #8,825 of 10,001 by trust here, #3,362 by citations
• Statistics and Probability — 9 citations into it; #11,515 of 12,932 by trust here, #3,893 by citations
• Control and Systems Engineering — 6 citations into it

Whom Ryan M. Rifkin trusts overall

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

1. Yuhai Wu
2. Grace Wahba
3. Tomaso A. Poggio
4. Federico Girosi
5. Theodoros Evgeniou
6. Koby Crammer
7. Dustin Boswell
8. Robert E. Schapire
9. Olivier Bousquet
10. Rohan Shah

Who trusts Ryan M. Rifkin overall

1. Jason Aaron Edward Weston
2. Jieping Ye
3. Samy Bengio
4. Chunhua Shen
5. Lisha Li
6. Hannes Nickisch
7. Zhuowen Tu
8. Bharath Hariharan
9. Jake Snell
10. Kristin P. Bennett

Papers from Ryan M. Rifkin's perspective

Ordered by how strongly the researchers Ryan M. Rifkin 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. Going deeper with convolutions
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• General conditions for predictivity in learning theory (2004) — 304 citations
• Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization (2006) — 241 citations
• Scalable Learning of Non-Decomposable Objectives (2016) — 122 citations
• Estimating Dataset Size Requirements for Classifying DNA Microarray Data (2003) — 301 citations
• Multiclass cancer diagnosis using tumor gene expression signatures (2001) — 2,146 citations
• Prediction of central nervous system embryonal tumour outcome based on gene expression (2002) — 2,465 citations
• Value Regularization and Fenchel Duality (2007) — 90 citations
• In Defense of One-Vs-All Classification (2004) — 2,069 citations