Anvita Gupta

Molecular Biology · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 16 citations into it; #6,364 of 17,773 by trust here, #9,030 by citations
• Computational Theory and Mathematics — 12 citations into it
• Materials Chemistry — 9 citations into it
• Artificial Intelligence — 8 citations into it
• Computational Chemistry & Drug Discovery — 8 citations into it
• Generative Models — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Anvita Gupta trusts overall

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

1. Martín Arjovsky
2. Dong Xu
3. Marwin H. S. Segler
4. X. Liu
5. Guangshun Wang
6. Cristóbal Esteban
7. Anton Osokin
8. Ishaan Gulrajani
9. Daniel W. A. Buchan
10. Peter Goldsborough

Who trusts Anvita Gupta overall

1. Kevin Kaichuang Yang
2. David H. Brookes
3. Jie Gui
4. Tânia F Cova
5. Francesca Grisoni
6. Zaccary Alperstein
7. Zachary Wu
8. Peter Ertl
9. Hahnbeom Park
10. Zhenan Sun

Papers from Anvita Gupta's perspective

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

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

Their papers

• Generative Recurrent Networks for De Novo Drug Design (2017) — 453 citations
• Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions (2018) — 62 citations