Carlo Lucibello

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 7 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #15,725 of 35,345 by trust here, #12,083 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Model Architectures — 8 citations into it

Whom Carlo Lucibello trusts overall

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

1. E. Gardner
2. Yann LeCun
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Max Welling
6. M. M'ezard
7. Carlo Baldassi
8. Alex Krizhevsky
9. Gintare Karolina Dziugaite
10. Sixin Zhang

Who trusts Carlo Lucibello overall

1. Carlo Baldassi
2. Pratik Chaudhari
3. Fabrizio Pittorino
4. Riccardo Zecchina
5. Gintare Karolina Dziugaite
6. Stefano Soatto

Papers from Carlo Lucibello's perspective

Ordered by how strongly the researchers Carlo Lucibello trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses. (2015) — 152 citations
• Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes (2016) — 183 citations
• Learning may need only a few bits of synaptic precision. (2016) — 29 citations
• Local entropy as a measure for sampling solutions in constraint satisfaction problems (2015) — 71 citations
• Entropic gradient descent algorithms and wide flat minima (2020) — 40 citations