Adrian Carrio

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 142 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 48 citations into it
• Artificial Intelligence — 38 citations into it
• Aerospace Engineering — 20 citations into it
• Reinforcement Learning — 9 citations into it
• Model Architectures — 7 citations into it
• Control and Systems Engineering — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Robotics & Control — 5 citations into it

Whom Adrian Carrio trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Ross B. Girshick
5. Sergey Levine
6. Ilya Sutskever
7. Yann LeCun
8. Diederik P. Kingma
9. Alex Graves
10. Kaiming He

Who trusts Adrian Carrio overall

1. Hazim Shakhatreh

Papers from Adrian Carrio's perspective

Ordered by how strongly the researchers Adrian Carrio trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Stock Price Movement Prediction from Financial News with Deep Learning and Knowledge Graph Embedding (2018) — 44 citations
• A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques (2018) — 163 citations
• A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles (2017) — 339 citations