P. Lorenzo
Computer Networks and Communications · with Barbarossa · 2014–2016
A researcher in the Science corpus: 4 papers; trusts 76 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 50 citations into it; #521 of 9,886 by trust here, #1,645 by citations
- Artificial Intelligence — 31 citations into it; #34,218 of 35,345 by trust here, #27,551 by citations
- Computational Mechanics — 22 citations into it; #838 of 10,001 by trust here, #2,782 by citations
- Optimization & Training — 21 citations into it
- Electrical and Electronic Engineering — 15 citations into it; #6,430 of 16,539 by trust here, #9,210 by citations
- Statistical and Nonlinear Physics — 11 citations into it; #9,771 of 10,127 by trust here, #8,564 by citations
- Computer Vision and Pattern Recognition — 9 citations into it
- Computational Theory and Mathematics — 7 citations into it; #11,831 of 12,182 by trust here, #9,804 by citations
Whom P. Lorenzo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Angelia Nedić
- Ali H. Sayed
- Yoshua Bengio
- Pascal Bianchi
- Wei Shi
- Alexander Olshevsky
- Xavier Glorot
- John N. Tsitsiklis
- Asuman E. Ozdaglar
- Juan Andrés Bazerque
Who trusts P. Lorenzo overall
- Angelia Nedić
- Alexander Olshevsky
- Guannan Qu
- Kun Yuan
- Shi Pu
- Bicheng Ying
- Simone Scardapane
- Thinh T. Doan
- Albert S. Berahas
- Chang-Shen Lee
Papers from P. Lorenzo's perspective
Ordered by how strongly the researchers P. Lorenzo 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks (2014) — 451 citations
- Signals on Graphs: Uncertainty Principle and Sampling (2015) — 347 citations
- NEXT: In-Network Nonconvex Optimization (2016) — 572 citations
- A Framework for Parallel and Distributed Training of Neural Networks (2016) — 27 citations