Tommaso Furlanello

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 206 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #8,608 of 35,345 by trust here, #4,420 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #8,105 of 21,575 by trust here, #3,765 by citations
• Model Architectures — 31 citations into it; #1,094 of 2,278 by trust here, #1,475 by citations
• Optimization & Training — 26 citations into it
• Computational Mathematics — 17 citations into it
• Efficiency & Compression — 15 citations into it
• Signal Processing — 15 citations into it
• Natural Language Processing — 7 citations into it

Whom Tommaso Furlanello trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Jimmy Ba
8. Sepp Hochreiter
9. Diederik P. Kingma
10. Ross B. Girshick

Who trusts Tommaso Furlanello overall

1. Chenglin Yang
2. Umberto Michieli
3. Seonguk Park
4. Amanda Rios
5. Lingxi Xie
6. Linfeng Zhang
7. Xu Tan
8. Li Yuan
9. Vincenzo Lomonaco
10. Jason Ramapuram

Papers from Tommaso Furlanello's perspective

Ordered by how strongly the researchers Tommaso Furlanello 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Born Again Neural Networks (2018) — 1,252 citations
• Active Long Term Memory Networks (2016) — 44 citations
• Tensor Regression Networks (2017) — 173 citations
• Learning Causal State Representations of Partially Observable Environments (2019) — 60 citations