T. Shukla

Computational Theory and Mathematics · 2020

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 26 citations into it
• Molecular Biology — 22 citations into it
• Materials Chemistry — 10 citations into it
• Computational Chemistry & Drug Discovery — 5 citations into it

Whom T. Shukla trusts overall

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

1. Marwin H. S. Segler
2. Corinna Cortes
3. Mark P. Waller

Papers from T. Shukla's perspective

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

1. Dropout: a simple way to prevent neural networks from overfitting
2. Support-Vector Networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Deep Learning
6. A renaissance of neural networks in drug discovery
7. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
8. Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction.
9. The rise of deep learning in drug discovery. (Drug Discovery Today)
10. Planning chemical syntheses with deep neural networks and symbolic AI

Their papers

• Machine Learning Methods in Drug Discovery (2020) — 324 citations