Andreas Argyriou

Statistical Learning Theory · 2007–2010

A researcher in the Science corpus: 5 papers; trusts 36 researchers and is trusted by 186 researchers here, as inferred from the citation graph.

Subfields

• Statistical Learning Theory — 25 citations into it
• Artificial Intelligence — 17 citations into it; #1,443 of 35,345 by trust here, #3,362 by citations
• Representation & Self-Supervised Learning — 17 citations into it; #195 of 3,145 by trust here, #500 by citations
• Computational Mechanics — 13 citations into it; #126 of 10,001 by trust here, #225 by citations
• Optimization & Training — 12 citations into it; #204 of 2,601 by trust here, #347 by citations
• Statistics and Probability — 11 citations into it
• Computational Theory and Mathematics — 9 citations into it; #6,533 of 12,182 by trust here, #4,696 by citations
• Computer Vision and Pattern Recognition — 8 citations into it

Whom Andreas Argyriou trusts overall

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

1. Charles A. Micchelli
2. Jonathan Baxter
3. Andreas Maurer
4. Massimiliano Pontil
5. Nati Srebro
6. Grace Wahba
7. Theodoros Evgeniou
8. Bart J. Bakker
9. Vladimir Naumovich Vapni
10. David L. Donoho

Who trusts Andreas Argyriou overall

1. Yu Zhang
2. Andreas Maurer
3. Massimiliano Pontil
4. Qiang Yang
5. Jiayu Zhou
6. Dit-Yan Yeung
7. Yongxin Yang
8. Bernardino Romera-Paredes
9. Anastasia Pentina
10. Carlo Ciliberto

Papers from Andreas Argyriou's perspective

Ordered by how strongly the researchers Andreas Argyriou 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Convex multi-task feature learning (2008) — 1,616 citations
• An Algorithm for Transfer Learning in a Heterogeneous Environment (2008) — 86 citations
• On Spectral Learning (2010) — 310 citations
• When is there a representer theorem? Vector versus matrix regularizers (2008) — 174 citations
• A Spectral Regularization Framework for Multi-Task Structure Learning (2007) — 253 citations