Vijil Chenthamarakshan

Molecular Biology · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 68 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 93 citations into it
• Artificial Intelligence — 83 citations into it
• Materials Chemistry — 62 citations into it
• Computational Theory and Mathematics — 47 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it; #11,745 of 21,575 by trust here, #14,478 by citations
• Computational Biology & Bioinformatics — 32 citations into it
• Generative Models — 29 citations into it; #1,078 of 1,956 by trust here, #1,531 by citations
• Graph Learning — 29 citations into it

Whom Vijil Chenthamarakshan trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Alex Krizhevsky
6. Ian J. Goodfellow
7. Yann LeCun
8. Ilya Sutskever
9. Max Welling
10. Oriol Vinyals

Who trusts Vijil Chenthamarakshan overall

1. Vikram V. Ramaswamy
2. Divya Saxena
3. Simon Caton

Papers from Vijil Chenthamarakshan's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. GENERATIVE ADVERSARIAL NETS
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Fairness GAN (2018) — 98 citations
• Protein Representation Learning by Geometric Structure Pretraining (2022) — 328 citations
• Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations (2021) — 398 citations
• Do Large Scale Molecular Language Representations Capture Important Structural Information? (2021) — 13 citations
• PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences (2018) — 69 citations