Andrea Caponnetto
Statistical Learning Theory · 2004–2008
A researcher in the Science corpus: 9 papers; trusts 36 researchers and is trusted by 75 researchers here, as inferred from the citation graph.
Subfields
- Statistical Learning Theory — 49 citations into it; #191 of 1,038 by trust here, #178 by citations
- Artificial Intelligence — 44 citations into it; #5,515 of 35,345 by trust here, #6,664 by citations
- Statistics and Probability — 29 citations into it; #1,298 of 12,932 by trust here, #1,688 by citations
- Applied Mathematics — 20 citations into it; #5,400 of 7,620 by trust here, #5,097 by citations
- Mathematical Physics — 20 citations into it; #240 of 8,562 by trust here, #459 by citations
- Computational Theory and Mathematics — 15 citations into it
- Optimization & Training — 12 citations into it; #318 of 2,601 by trust here, #455 by citations
- Computational Mechanics — 10 citations into it; #375 of 10,001 by trust here, #593 by citations
Whom Andrea Caponnetto trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ernesto De Vito
- Steve Smale
- Theodoros Evgeniou
- Lorenzo Rosasco
- Grace Wahba
- Tomaso A. Poggio
- Heinz W. Engl
- Massimiliano Pontil
- Laurent Schwartz
- Charles A. Micchelli
Who trusts Andrea Caponnetto overall
- Lorenzo Rosasco
- Ernesto De Vito
- Tengyuan Liang
- Francis R. Bach
- Alessandro Rudi
- Kenji Fukumizu
- Ingo Steinwart
- Luca Baldassarre
- Hachem Kadri
- Bharath K. Sriperumbudur
Papers from Andrea Caponnetto's perspective
Ordered by how strongly the researchers Andrea Caponnetto 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
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- A Probabilistic Theory of Pattern Recognition
- The Nature of Statistical Learning Theory
- Long Short-Term Memory
- Spline Models for Observational Data
Their papers
- On Early Stopping in Gradient Descent Learning (2007) — 1,149 citations
- Are Loss Functions All the Same? (2004) — 527 citations
- Universal Multi-Task Kernels (2008) — 167 citations
- Optimal Rates for Regularization Operators in Learning Theory (2006) — 40 citations
- Optimal Rates for the Regularized Least-Squares Algorithm (2007) — 961 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