Alessandro Verri

Artificial Intelligence · 2003–2012

A researcher in the Science corpus: 10 papers; trusts 66 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #12,668 of 35,345 by trust here, #15,866 by citations
• Statistical Learning Theory — 56 citations into it
• Computational Mechanics — 46 citations into it; #6,352 of 10,001 by trust here, #2,715 by citations
• Statistics and Probability — 41 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it; #5,982 of 21,575 by trust here, #9,563 by citations
• Mathematical Physics — 26 citations into it; #7,010 of 8,562 by trust here, #3,335 by citations
• Optimization & Training — 24 citations into it
• Computational Theory and Mathematics — 22 citations into it

Whom Alessandro Verri trusts overall

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

1. Teuvo Kohonen
2. Vladimir Naumovich Vapnik
3. Bernhard Schölkopf
4. Grace Wahba
5. Theodoros Evgeniou
6. Eric R. Ziegel
7. Dustin Boswell
8. Charles A. Micchelli
9. Andrea Caponnetto
10. Massimiliano Pontil

Who trusts Alessandro Verri overall

1. Sun-Yuan Kung
2. Daniel Graves
3. Krikamol Muandet
4. Bharath K. Sriperumbudur
5. Lorenzo Rosasco
6. Andrea Caponnetto
7. Hsin-Chien Huang
8. Maurizio Filippone
9. Lee H. Dicker
10. Bei Zhao

Papers from Alessandro Verri's perspective

Ordered by how strongly the researchers Alessandro Verri 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

• Histogram intersection kernel for image classification (2003) — 354 citations
• Are Loss Functions All the Same? (2004) — 527 citations
• Multi-output learning via spectral filtering (2012) — 67 citations
• Nonparametric sparsity and regularization (2012) — 109 citations
• Vector Field Learning via Spectral Filtering (2010) — 30 citations
• A Regularization Approach to Nonlinear Variable Selection (2010) — 29 citations
• Spectral Algorithms for Supervised Learning (2008) — 172 citations
• Spectral Methods for Regularization in Learning Theory (2006)
• A novel kernel method for clustering (2005) — 351 citations
• Some Properties of Regularized Kernel Methods (2004) — 103 citations