Joseph Gomes

Computational Theory and Mathematics · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 35 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 120 citations into it; #6,342 of 12,182 by trust here, #663 by citations
• Computational Chemistry & Drug Discovery — 102 citations into it; #183 of 425 by trust here, #88 by citations
• Molecular Biology — 92 citations into it; #7,163 of 17,773 by trust here, #4,132 by citations
• Artificial Intelligence — 87 citations into it; #27,114 of 35,345 by trust here, #10,667 by citations
• Materials Chemistry — 81 citations into it; #2,312 of 11,513 by trust here, #2,445 by citations
• Graph Learning — 54 citations into it; #462 of 693 by trust here, #201 by citations
• Representation & Self-Supervised Learning — 40 citations into it; #2,541 of 3,145 by trust here, #1,415 by citations
• Computer Vision and Pattern Recognition — 35 citations into it

Whom Joseph Gomes trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yann LeCun
5. Yoshua Bengio
6. Ilya Sutskever
7. Sergey Ioffe
8. Jimmy Ba
9. William L. Hamilton
10. Max Welling

Who trusts Joseph Gomes overall

1. Chaopeng Shen
2. Raphael J. L. Townshend
3. Ruoyu Li
4. Weihua Hu
5. Garrett B. Goh
6. Andreas Tosstorff
7. Seyone Chithrananda
8. Travers Ching
9. William L. Hamilton
10. Kyle Swanson

Papers from Joseph Gomes's perspective

Ordered by how strongly the researchers Joseph Gomes 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Strategies for Pre-training Graph Neural Networks (2019) — 1,844 citations
• MoleculeNet: a benchmark for molecular machine learning (2017) — 2,566 citations
• Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models (2017) — 486 citations
• Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity (2017) — 208 citations
• Pre-training Graph Neural Networks (2019) — 52 citations
• enchmark for molecular machine learning † (2017)
• Deep Learning the Physics of Transport Phenomena (2017) — 117 citations