Vikas K. Garg

Molecular Biology · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 30 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 38 citations into it
• Artificial Intelligence — 35 citations into it; #27,659 of 35,345 by trust here, #25,809 by citations
• Computational Biology & Bioinformatics — 22 citations into it
• Graph Learning — 22 citations into it
• Materials Chemistry — 16 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Generative Models — 10 citations into it
• Model Architectures — 10 citations into it

Whom Vikas K. Garg trusts overall

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

1. Zhengdao Chen
2. Andreas Loukas
3. Geoffrey E. Hinton
4. Thomas Kipf
5. Yoshua Bengio
6. Diederik P. Kingma
7. William L. Hamilton
8. Alex Krizhevsky
9. Max Welling
10. Justin Gilmer

Who trusts Vikas K. Garg overall

1. Christopher Morris
2. Gaurav Rattan
3. Kevin Kaichuang Yang
4. Michael Jendrusch
5. Zhangyang Gao
6. Johannes Klicpera
7. Alex Morehead
8. Bowen Jing
9. Wenhao Gao
10. Wengong Jin

Papers from Vikas K. Garg's perspective

Ordered by how strongly the researchers Vikas K. Garg 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Generalization and Representational Limits of Graph Neural Networks (2020) — 372 citations
• Generative Models for Graph-Based Protein Design (2019) — 631 citations