Arantxa Casanova

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 3 papers; trusts 377 researchers and is trusted by 83 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 99 citations into it
• Artificial Intelligence — 59 citations into it; #3,240 of 35,345 by trust here, #1,215 by citations
• Generative Models — 31 citations into it
• Model Architectures — 24 citations into it; #339 of 2,278 by trust here, #151 by citations
• Graph Learning — 17 citations into it; #108 of 693 by trust here, #17 by citations
• Optimization & Training — 12 citations into it
• Natural Language Processing — 9 citations into it
• Biophysics — 7 citations into it

Whom Arantxa Casanova trusts overall

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

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

Who trusts Arantxa Casanova overall

1. Matthias Fey
2. Vijay Prakash Dwivedi
3. Rex Ying
4. Florian Bordes
5. Yoshua Bengio
6. Jiaxuan You
7. William L. Hamilton
8. Jure Leskovec
9. Michihiro Yasunaga
10. Prithviraj Ammanabrolu

Papers from Arantxa Casanova's perspective

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

Their papers

• Graph Attention Networks (2017) — 27,282 citations
• Instance-Conditioned GAN (2021) — 164 citations
• On the Iterative Refinement of Densely Connected Representation Levels for Semantic Segmentation (2018) — 26 citations