Ernesto De Vito

Statistical Learning Theory · 2004–2012

A researcher in the Science corpus: 13 papers; trusts 62 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Statistical Learning Theory — 78 citations into it; #166 of 1,038 by trust here, #164 by citations
• Artificial Intelligence — 68 citations into it
• Statistics and Probability — 57 citations into it
• Mathematical Physics — 34 citations into it; #217 of 8,562 by trust here, #226 by citations
• Applied Mathematics — 33 citations into it; #1,372 of 7,620 by trust here, #810 by citations
• Computational Theory and Mathematics — 30 citations into it; #5,181 of 12,182 by trust here, #2,931 by citations
• Computational Mechanics — 25 citations into it; #450 of 10,001 by trust here, #572 by citations
• Computer Vision and Pattern Recognition — 23 citations into it

Whom Ernesto De Vito trusts overall

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

1. Steve Smale
2. Andrea Caponnetto
3. Grace Wahba
4. Theodoros Evgeniou
5. Eric R. Ziegel
6. Ingo Steinwart
7. Tomaso A. Poggio
8. Laurent Schwartz
9. Ding-Xuan Zhou
10. Felipe Cucker

Who trusts Ernesto De Vito overall

1. Andrea Caponnetto
2. Lorenzo Rosasco
3. Bharath K. Sriperumbudur
4. Krikamol Muandet
5. Kenji Fukumizu
6. Alessandro Rudi
7. Francis R. Bach
8. Arthur Gretton
9. Tengyuan Liang
10. Frank Bauer

Papers from Ernesto De Vito's perspective

Ordered by how strongly the researchers Ernesto De Vito 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Are Loss Functions All the Same? (2004) — 527 citations
• On Learning with Integral Operators (2010) — 263 citations
• Vector valued reproducing kernel Hilbert spaces and universality (2008) — 236 citations
• Elastic-net regularization in learning theory (2008) — 346 citations
• Learning Sets with Separating Kernels (2012) — 57 citations
• Spectral Algorithms for Supervised Learning (2008) — 172 citations
• Optimal Rates for the Regularized Least-Squares Algorithm (2007) — 961 citations
• Spectral Methods for Regularization in Learning Theory (2006)
• VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES OF INTEGRABLE FUNCTIONS AND MERCER THEOREM (2006) — 274 citations
• DISCRETIZATION ERROR ANALYSIS FOR TIKHONOV REGULARIZATION (2006) — 38 citations
• Learning from Examples as an Inverse Problem (2005) — 262 citations
• Learning, Regularization and Ill-Posed Inverse Problems (2004) — 17 citations
• Some Properties of Regularized Kernel Methods (2004) — 103 citations