Lorenzo Porzi
Computer Vision and Pattern Recognition · 2016–2019
A researcher in the Science corpus: 5 papers; trusts 835 researchers and is trusted by 474 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 142 citations into it; #1,927 of 21,575 by trust here, #3,955 by citations
- Artificial Intelligence — 78 citations into it; #6,817 of 35,345 by trust here, #7,858 by citations
- Model Architectures — 25 citations into it
- Aerospace Engineering — 20 citations into it
- Evaluation & Benchmarks — 17 citations into it
- Optimization & Training — 17 citations into it; #815 of 2,601 by trust here, #1,154 by citations
- Representation & Self-Supervised Learning — 17 citations into it
- Efficiency & Compression — 8 citations into it
Whom Lorenzo Porzi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ross B. Girshick
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Shaoqing Ren
- Diederik P. Kingma
- Karen Simonyan
Who trusts Lorenzo Porzi overall
- Liang-Chieh Chen
- Fabio Cermelli
- Yuhui Yuan
- Huiyu Wang
- Bowen Cheng
- Siyuan Qiao
- Massimiliano Mancini
- Rohit Mohan
- Yukun Zhu
- Alexander Kirillov
Papers from Lorenzo Porzi's perspective
Ordered by how strongly the researchers Lorenzo Porzi 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)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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
- Going deeper with convolutions
Their papers
- In-place Activated BatchNorm for Memory-Optimized Training of DNNs (2017) — 377 citations
- AutoDIAL: Automatic Domain Alignment Layers (2017) — 335 citations
- Disentangling Monocular 3D Object Detection (2019) — 511 citations
- Seamless Scene Segmentation (2019) — 231 citations
- Dropout distillation (2016) — 48 citations