Andrea Censi
Electrical and Electronic Engineering · 2008–2019
A researcher in the Science corpus: 4 papers; trusts 43 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 45 citations into it; #1,738 of 16,539 by trust here, #12,348 by citations
- Computer Vision and Pattern Recognition — 31 citations into it; #6,114 of 21,575 by trust here, #16,787 by citations
- Artificial Intelligence — 20 citations into it; #25,085 of 35,345 by trust here, #34,943 by citations
- Cognitive Neuroscience — 20 citations into it
- Aerospace Engineering — 17 citations into it; #947 of 6,421 by trust here, #4,066 by citations
- Control and Systems Engineering — 11 citations into it; #5,551 of 8,257 by trust here, #7,501 by citations
- Reinforcement Learning — 7 citations into it
- Representation & Self-Supervised Learning — 6 citations into it
Whom Andrea Censi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Roland Memisevic
- Ilya Sutskever
- Marc'Aurelio Ranzato
- Gregory S. Chirikjian
- Hugo Larochelle
- Yoshua Bengio
- Richard S. Sutton
- Benjamin Kuipers
- Dmitry Chetverikov
Who trusts Andrea Censi overall
- John Kua
- Sajad Saeedi
- Heng Yang
- Chelsea Finn
- Teodor Tomic
- Edwin Olson
- Nicholas Corso
- Sergey Levine
- Michael Trentini
- Pasquale Antonante
Papers from Andrea Censi's perspective
Ordered by how strongly the researchers Andrea Censi 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)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Bootstrapping bilinear models of Simple Vehicles (2015) — 7 citations
- An ICP variant using a point-to-line metric (2008) — 667 citations
- Event-Based Vision: A Survey (2019) — 2,710 citations
- Duckietown: An open, inexpensive and flexible platform for autonomy education and research (2017) — 247 citations