Pedro Hermosilla

Computational Mechanics · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 23 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Molecular Biology — 21 citations into it
• Geology — 18 citations into it
• Computational Biology & Bioinformatics — 16 citations into it
• Artificial Intelligence — 13 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Graph Learning — 7 citations into it

Whom Pedro Hermosilla trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Angela Dai
5. Charles Ruizhongtai Qi
6. Yoshua Bengio
7. Emanuel Parzen
8. Rémi Bardenet
9. Ilya Sutskever
10. Fabian Groh

Who trusts Pedro Hermosilla overall

1. Zhiyuan Zhang
2. Yifan Wang
3. Wenxuan Wu
4. Benjamin Ummenhofer
5. Yongheng Zhao
6. Xu Yan
7. Vignesh Ram Somnath
8. Meng-Hao Guo
9. Haozhe Xie
10. Hugues Thomas

Papers from Pedro Hermosilla's perspective

Ordered by how strongly the researchers Pedro Hermosilla 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Monte Carlo convolution for learning on non-uniformly sampled point clouds (2018) — 282 citations
• Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures (2021) — 124 citations