Mattia Segu
Computer Vision and Pattern Recognition · 2019–2024
A researcher in the Science corpus: 2 papers; trusts 153 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 59 citations into it; #13,650 of 21,575 by trust here, #15,955 by citations
- Artificial Intelligence — 35 citations into it; #22,033 of 35,345 by trust here, #29,412 by citations
- Probabilistic & Bayesian Methods — 17 citations into it; #1,124 of 1,525 by trust here, #1,313 by citations
- Aerospace Engineering — 16 citations into it
- Robotics & Control — 13 citations into it
- Automotive Engineering — 11 citations into it
- Reinforcement Learning — 10 citations into it
- Optimization & Training — 6 citations into it
Whom Mattia Segu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Radford M. Neal
- Yarin Gal
- Yann LeCun
- Diederik P. Kingma
- Yoshua Bengio
- Kaiming He
- Ilya Sutskever
- Matthias Faessler
Who trusts Mattia Segu overall
Papers from Mattia Segu's perspective
Ordered by how strongly the researchers Mattia Segu 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
- ImageNet: A large-scale hierarchical image database
- 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
- Learning Multiple Layers of Features from Tiny Images
Their papers
- UniDepth: Universal Monocular Metric Depth Estimation (2024) — 455 citations
- A General Framework for Uncertainty Estimation in Deep Learning (2019) — 354 citations