Carlo Ciliberto

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 14 papers; trusts 469 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 187 citations into it; #10,936 of 35,345 by trust here, #9,170 by citations
• Computer Vision and Pattern Recognition — 115 citations into it; #12,013 of 21,575 by trust here, #12,419 by citations
• Optimization & Training — 59 citations into it
• Computational Mechanics — 58 citations into it; #5,714 of 10,001 by trust here, #5,567 by citations
• Representation & Self-Supervised Learning — 51 citations into it
• Statistical Learning Theory — 47 citations into it
• Computational Theory and Mathematics — 32 citations into it
• Statistics and Probability — 24 citations into it

Whom Carlo Ciliberto trusts overall

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

1. Geoffrey E. Hinton
2. Andreas Maurer
3. Alex Krizhevsky
4. Christopher K. I. Williams
5. Radford M. Neal
6. Yoshua Bengio
7. Massimiliano Pontil
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Carlo Ciliberto overall

1. Vincenzo Lomonaco
2. Mingsheng Long
3. Mikhail Khodak
4. Giulia Denevi
5. Tyler L. Hayes
6. German Ignacio Parisi
7. Luigi Carratino
8. Kaiyi Ji
9. Davide Maltoni
10. Jianmin Wang

Papers from Carlo Ciliberto's perspective

Ordered by how strongly the researchers Carlo Ciliberto 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Object identification from few examples by improving the invariance of a Deep Convolutional Neural Network (2016) — 59 citations
• Learning-to-Learn Stochastic Gradient Descent with Biased Regularization (2019) — 120 citations
• Incremental Learning-to-Learn with Statistical Guarantees (2018) — 48 citations
• Convex Learning of Multiple Tasks and their Structure (2015) — 70 citations
• Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation (2019) — 69 citations
• Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm (2019) — 71 citations
• Learning To Learn Around A Common Mean (2018) — 85 citations
• Quantum machine learning: a classical perspective (2017) — 544 citations
• Incremental Object Recognition in Robotics with Extension to New Classes in Constant Time (2016) — 5 citations
• A Consistent Regularization Approach for Structured Prediction (2016) — 86 citations
• Incremental robot learning of new objects with fixed update time (2016) — 44 citations
• Learning multiple visual tasks while discovering their structure (2015) — 17 citations
• Teaching iCub to recognize objects using deep Convolutional Neural Networks (2015) — 55 citations
• iCub World: Friendly Robots Help Building Good Vision Data-Sets (2013) — 31 citations