Amol Thakkar

Computational Theory and Mathematics · 2020

A researcher in the Science corpus: 2 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 37 citations into it
• Materials Chemistry — 28 citations into it
• Molecular Biology — 23 citations into it

Whom Amol Thakkar trusts overall

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

1. Esben Jannik Bjerrum
2. Nadine Schneider
3. Marwin H. S. Segler
4. Justin Gilmer

Papers from Amol Thakkar's perspective

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

1. Convolutional Networks on Graphs for Learning Molecular Fingerprints
2. Long Short-Term Memory
3. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
4. Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)
5. Learning Deep Generative Models of Graphs
6. Relational inductive biases, deep learning, and graph networks
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet classification with deep convolutional neural networks
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning (2020) — 333 citations
• Molecular representations in AI-driven drug discovery: a review and practical guide (2020) — 482 citations