Olga Diamanti

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 2 papers; trusts 159 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 66 citations into it; #5,871 of 21,575 by trust here, #5,843 by citations
• Generative Models — 45 citations into it
• Computational Mechanics — 31 citations into it; #8,729 of 10,001 by trust here, #7,501 by citations
• Artificial Intelligence — 28 citations into it
• Computer Graphics and Computer-Aided Design — 11 citations into it; #1,356 of 1,584 by trust here, #1,205 by citations
• Geology — 10 citations into it
• Optimization & Training — 9 citations into it
• Aerospace Engineering — 7 citations into it

Whom Olga Diamanti trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Andrew L. Maas
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Kaiming He
8. Charles Ruizhongtai Qi
9. Ilya Sutskever
10. Sergey Ioffe

Who trusts Olga Diamanti overall

1. Zhiqin Chen
2. Yan Zhang
3. Sung Woo Park
4. Michael Niemeyer
5. Yongming Rao
6. Heewoo Jun
7. Shitong Luo
8. Jonathan Sauder
9. Vincent Sitzmann
10. Minghua Liu

Papers from Olga Diamanti's perspective

Ordered by how strongly the researchers Olga Diamanti 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning Representations and Generative Models for 3D Point Clouds (2017) — 1,682 citations
• Representation Learning and Adversarial Generation of 3D Point Clouds (2017) — 92 citations