Aurelio Uncini

Artificial Intelligence · 1993–2020

A researcher in the Science corpus: 8 papers; trusts 254 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #23,143 of 35,345 by trust here, #20,377 by citations
• Computer Vision and Pattern Recognition — 101 citations into it; #9,893 of 21,575 by trust here, #11,083 by citations
• Optimization & Training — 55 citations into it
• Model Architectures — 40 citations into it
• Efficiency & Compression — 36 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Statistics and Probability — 11 citations into it
• Computational Mechanics — 10 citations into it; #6,775 of 10,001 by trust here, #4,749 by citations

Whom Aurelio Uncini trusts overall

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

1. Geoffrey E. Hinton
2. Ronald J. Williams
3. Yoshua Bengio
4. Alex Krizhevsky
5. J. Urgen Schmidhuber
6. Yann LeCun
7. Diederik P. Kingma
8. Kaiming He
9. Ilya Sutskever
10. David E. Rumelhart

Who trusts Aurelio Uncini overall

1. Jiangpeng He
2. Enrico Zio
3. Simone Scardapane
4. Sangwon Jung

Papers from Aurelio Uncini's perspective

Ordered by how strongly the researchers Aurelio Uncini 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Group sparse regularization for deep neural networks (2016) — 500 citations
• Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications (2020) — 32 citations
• Why Should We Add Early Exits to Neural Networks? (2020) — 152 citations
• Differentiable Branching In Deep Networks for Fast Inference (2020) — 32 citations
• Efficient Continual Learning in Neural Networks with Embedding Regularization (2019) — 47 citations
• Distributed learning for Random Vector Functional-Link networks (2015) — 148 citations
• On-line learning algorithms for locally recurrent neural networks (1999)
• Fast neural networks without multipliers (1993)