Francisco Manuel Castro

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 68 researchers and is trusted by 93 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #3,382 of 35,345 by trust here, #3,009 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #3,208 of 21,575 by trust here, #2,263 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Optimization & Training — 9 citations into it
• Biomedical Engineering — 7 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Francisco Manuel Castro trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Max Welling
4. Yoshua Bengio
5. Kaiming He
6. Karen Simonyan
7. Yann LeCun
8. Diederik P. Kingma
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Francisco Manuel Castro overall

1. Ali Ayub
2. Jiangpeng He
3. Eden Belouadah
4. Tyler L. Hayes
5. K. J. Joseph
6. Gyuhak Kim
7. Yaoyao Liu
8. Da-Wei Zhou
9. Arthur Douillard
10. Alan R. Wagner

Papers from Francisco Manuel Castro's perspective

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

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

Their papers

• End-to-End Incremental Learning (2018) — 1,337 citations
• iLGaCo: Incremental Learning of Gait Covariate Factors (2020) — 7 citations