Giulia Pasquale

Computer Vision and Pattern Recognition · 2015–2016

A researcher in the Science corpus: 4 papers; trusts 108 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 44 citations into it; #14,426 of 21,575 by trust here, #16,787 by citations
• Artificial Intelligence — 37 citations into it
• Model Architectures — 10 citations into it
• Statistical Learning Theory — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Giulia Pasquale trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yangqing Jia
4. Ilya Sutskever
5. Matthew D. Zeiler
6. Karen Simonyan
7. Christopher K. I. Williams
8. Kaiming He
9. Yann LeCun
10. Rob Fergus

Who trusts Giulia Pasquale overall

1. Vincenzo Lomonaco
2. German Ignacio Parisi
3. Davide Maltoni
4. Oliver Lemon
5. Timothée Lesort
6. Mennatullah Siam
7. Raffaello Camoriano
8. Massimiliano Mancini
9. Marco Melis
10. Andrei Stoian

Papers from Giulia Pasquale's perspective

Ordered by how strongly the researchers Giulia Pasquale 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. ImageNet Large Scale Visual Recognition Challenge
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Object identification from few examples by improving the invariance of a Deep Convolutional Neural Network (2016) — 59 citations
• Incremental Object Recognition in Robotics with Extension to New Classes in Constant Time (2016) — 5 citations
• Incremental robot learning of new objects with fixed update time (2016) — 44 citations
• Teaching iCub to recognize objects using deep Convolutional Neural Networks (2015) — 55 citations