Maurizio Filippone

Artificial Intelligence · 2008–2018

A researcher in the Science corpus: 7 papers; trusts 117 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #7,423 of 35,345 by trust here, #9,760 by citations
• Probabilistic & Bayesian Methods — 48 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it; #12,468 of 21,575 by trust here, #10,500 by citations
• Statistics and Probability — 30 citations into it; #6,250 of 12,932 by trust here, #7,165 by citations
• Control and Systems Engineering — 18 citations into it; #5,552 of 8,257 by trust here, #3,004 by citations
• Optimization & Training — 18 citations into it
• Statistical Learning Theory — 13 citations into it
• Computational Theory and Mathematics — 11 citations into it; #8,537 of 12,182 by trust here, #7,142 by citations

Whom Maurizio Filippone trusts overall

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

1. Radford M. Neal
2. Iain Murray
3. Inderjit S. Dhillon
4. Geoffrey E. Hinton
5. Teuvo Kohonen
6. Christopher K. I. Williams
7. Yoshua Bengio
8. Tom Minka
9. Michael I. Jordan
10. Diederik P. Kingma

Who trusts Maurizio Filippone overall

1. Francis R. Bach
2. Sergios Agapiou
3. Lassi Roininen
4. Maria Brbic
5. Dijana Tolic
6. Mark A. Girolami
7. Ivica Kopriva
8. John Paparrizos
9. Hugh Salimbeni
10. Chris J. Oates

Papers from Maurizio Filippone's perspective

Ordered by how strongly the researchers Maurizio Filippone 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A survey of kernel and spectral methods for clustering (2008) — 911 citations
• Random Feature Expansions for Deep Gaussian Processes (2016) — 153 citations
• MCMC for Variationally Sparse Gaussian Processes (2015) — 149 citations
• Pseudo-Marginal Bayesian Inference for Gaussian Processes (2013) — 66 citations
• Team Deep Neural Networks for Interference Channels (2018) — 44 citations
• A comparative evaluation of outlier detection algorithms: Experiments and analyses (2018) — 432 citations
• Calibrating Deep Convolutional Gaussian Processes (2018) — 45 citations