Michele M. Franceschini

Artificial Intelligence · 2009–2018

A researcher in the Science corpus: 5 papers; trusts 66 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it
• Electrical and Electronic Engineering — 34 citations into it
• Materials Chemistry — 30 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Reinforcement Learning — 13 citations into it
• Generative Models — 11 citations into it
• Meta & Continual Learning — 10 citations into it

Whom Michele M. Franceschini trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Yann LeCun
5. Alex Graves
6. Alex Krizhevsky
7. Ruslan Salakhutdinov
8. Ilya Sutskever
9. Radford M. Neal
10. Oriol Vinyals

Who trusts Michele M. Franceschini overall

1. Matthew Riemer
2. Lucas Caccia
3. Ying Xu
4. Blake Camp
5. Ignacio Cases
6. Jaya Krishna Mandivarapu
7. Luiz E. Ramos

Papers from Michele M. Franceschini's perspective

Ordered by how strongly the researchers Michele M. Franceschini 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. 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. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Scalable Recollections for Continual Lifelong Learning (2017) — 76 citations
• Phase change memory technology (2010) — 1,007 citations
• Enhancing lifetime and security of PCM-based Main Memory with Start-Gap Wear Leveling (2009) — 759 citations
• Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning (2018) — 5 citations
• Generative Knowledge Distillation for General Purpose Function Compression (2017)