Mikaela Angelina Uy

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 398 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it; #10,575 of 21,575 by trust here, #18,266 by citations
• Aerospace Engineering — 43 citations into it; #2,324 of 6,421 by trust here, #4,945 by citations
• Computational Mechanics — 36 citations into it
• Geology — 33 citations into it; #309 of 687 by trust here, #320 by citations
• 3D Vision & Pose — 11 citations into it
• Artificial Intelligence — 9 citations into it
• Environmental Engineering — 9 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Mikaela Angelina Uy trusts overall

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

1. Charles Ruizhongtai Qi
2. Andrew Zisserman
3. Alex Krizhevsky
4. Radu Bogdan Rusu
5. Karen Simonyan
6. Geoffrey E. Hinton
7. Relja Arandjelovi'c
8. Ross B. Girshick
9. Josef Sivic
10. Kaiming He

Who trusts Mikaela Angelina Uy overall

1. Giseop Kim
2. Ali Cheraghian
3. Renrui Zhang
4. Yongming Rao
5. Saifullahi Aminu Bello
6. Zhe Liu
7. Niall O' Mahony
8. Shi Qiu
9. Xu Ma
10. Hanchen Wang

Papers from Mikaela Angelina Uy's perspective

Ordered by how strongly the researchers Mikaela Angelina Uy 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. ImageNet: A large-scale hierarchical image database
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data (2019) — 1,049 citations
• PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition (2018) — 702 citations