Matthew Ragoza

Computational Theory and Mathematics · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 26 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 57 citations into it; #10,076 of 12,182 by trust here, #2,489 by citations
• Molecular Biology — 46 citations into it; #12,846 of 17,773 by trust here, #8,791 by citations
• Computational Chemistry & Drug Discovery — 33 citations into it
• Materials Chemistry — 32 citations into it
• Artificial Intelligence — 22 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Generative Models — 13 citations into it
• Physical and Theoretical Chemistry — 5 citations into it

Whom Matthew Ragoza trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. David Weininger
4. Alex Krizhevsky
5. Yoshua Bengio
6. Alec Radford
7. Jimmy Ba
8. Yann LeCun
9. Ilya Sutskever
10. Benjamín Sánchez-Lengeling

Who trusts Matthew Ragoza overall

1. Shitong Luo
2. Hannes Stärk
3. Raphael J. L. Townshend
4. Tomohide Masuda
5. Vignesh Ram Somnath
6. Marta M. Stepniewska-Dziubinska
7. Miha Škalič
8. Hakime Öztürk
9. Hongming Chen
10. Shuangjia Zheng

Papers from Matthew Ragoza's perspective

Ordered by how strongly the researchers Matthew Ragoza trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Protein-Ligand Scoring with Convolutional Neural Networks (2016) — 313 citations
• GNINA 1.0: molecular docking with deep learning (2021) — 765 citations
• Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models (2020) — 61 citations
• Protein-Ligand Scoring with Convolutional Neural Networks (2017) — 465 citations