Soledad Villar

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #23,037 of 35,345 by trust here, #14,718 by citations
• Graph Learning — 52 citations into it
• Model Architectures — 22 citations into it
• Molecular Biology — 17 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Optimization & Training — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Soledad Villar trusts overall

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

1. Diederik P. Kingma
2. Joan Bruna
3. Zhengdao Chen
4. Haggai Maron
5. Sergey Ioffe
6. Geoffrey E. Hinton
7. Jin-Yi Cai
8. Yann LeCun
9. Yoshua Bengio
10. Jimmy Ba

Who trusts Soledad Villar overall

1. Vijay Prakash Dwivedi
2. Chaitanya K. Joshi
3. Wouter Kool
4. Zhengdao Chen
5. Xiyuan Wang
6. Pan Li
7. Christopher Morris
8. Thomas Laurent
9. Jiaxuan You
10. Kiran Koshy Thekumparampil

Papers from Soledad Villar's perspective

Ordered by how strongly the researchers Soledad Villar 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• On the equivalence between graph isomorphism testing and function approximation with GNNs (2019) — 311 citations
• Can graph neural networks count substructures? (2020) — 381 citations
• A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks (2017) — 127 citations
• Extracting information from fine-tuned weights