Marco Piccirilli
Artificial Intelligence · 2016–2017
A researcher in the Science corpus: 2 papers; trusts 309 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 67 citations into it; #9,497 of 35,345 by trust here, #8,999 by citations
- Computer Vision and Pattern Recognition — 67 citations into it; #6,639 of 21,575 by trust here, #6,371 by citations
- Representation & Self-Supervised Learning — 34 citations into it; #1,365 of 3,145 by trust here, #1,246 by citations
- Media Technology — 8 citations into it
- Signal Processing — 7 citations into it
- Optimization & Training — 6 citations into it
- Computational Mechanics — 5 citations into it
Whom Marco Piccirilli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yann LeCun
- Alex Krizhevsky
- Karen Simonyan
- Andrew Zisserman
- Yoshua Bengio
- Kaiming He
- Diederik P. Kingma
- Ross B. Girshick
- Ilya Sutskever
Who trusts Marco Piccirilli overall
- Saeid Motiian
- Kaiyang Zhou
- Haohan Wang
- Yongxin Yang
- Mei Wang
- Toshihiko Matsuura
- Atsutoshi Kumagai
- Gianfranco Doretto
- Qi-Yan Dou
- John Lambert
Papers from Marco Piccirilli's perspective
Ordered by how strongly the researchers Marco Piccirilli trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Unified Deep Supervised Domain Adaptation and Generalization (2017) — 889 citations
- Information Bottleneck Learning Using Privileged Information for Visual Recognition (2016) — 54 citations