Luca Baldassarre

Artificial Intelligence · 2010–2012

A researcher in the Science corpus: 4 papers; trusts 43 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #12,845 of 35,345 by trust here, #16,771 by citations
• Statistical Learning Theory — 34 citations into it
• Computational Mechanics — 18 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Control and Systems Engineering — 14 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it; #767 of 1,525 by trust here, #817 by citations
• Computer Vision and Pattern Recognition — 13 citations into it
• Mathematical Physics — 13 citations into it

Whom Luca Baldassarre trusts overall

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

1. Charles A. Micchelli
2. Massimiliano Pontil
3. Andrea Caponnetto
4. Alan J. Izenman
5. Theodoros Evgeniou
6. Alex Smola
7. Peter Buhlmann
8. Eric R. Ziegel
9. Andreas Argyriou
10. Tong Zhang

Who trusts Luca Baldassarre overall

1. Nan Jiang
2. Kenji Fukumizu
3. Yu Nishiyama
4. Céline Brouard
5. Arthur Gretton
6. Krikamol Muandet
7. Bo Dai
8. Hanen Borchani
9. Herke Van Hoof
10. Le Song

Papers from Luca Baldassarre's perspective

Ordered by how strongly the researchers Luca Baldassarre 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Modelling transition dynamics in MDPs with RKHS embeddings (2012) — 140 citations
• Conditional mean embeddings as regressors (2012) — 165 citations
• Multi-output learning via spectral filtering (2012) — 67 citations
• Vector Field Learning via Spectral Filtering (2010) — 30 citations