Saurabh Verma

Graph Learning · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 70 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Graph Learning — 42 citations into it
• Artificial Intelligence — 36 citations into it; #17,994 of 35,345 by trust here, #31,361 by citations
• Molecular Biology — 23 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it; #11,422 of 21,575 by trust here, #19,766 by citations
• Representation & Self-Supervised Learning — 8 citations into it
• Statistical and Nonlinear Physics — 8 citations into it; #4,550 of 10,127 by trust here, #8,564 by citations
• Materials Chemistry — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Saurabh Verma trusts overall

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

1. Chih-Jen Lin
2. Risi Kondor
3. Karsten M. Borgwardt
4. Geoffrey E. Hinton
5. Nino Shervashidze
6. Thomas Kipf
7. James Atwood
8. Thomas Gärtner
9. Mathias Niepert
10. Yann LeCun

Who trusts Saurabh Verma overall

1. Zhang Xinyi
2. Chen Cai
3. Lihu Chen
4. Fan-Yun Sun
5. Haggai Maron
6. Keyulu Xu
7. Zhen Zhang
8. Xiaoxiao Ma
9. Yusu Wang
10. Zhi-Li Zhang

Papers from Saurabh Verma's perspective

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

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

Their papers

• Transcriptional regulation and signalling of cold stress response in plants: An overview of current understanding (2020) — 98 citations
• Graph Capsule Convolutional Neural Networks (2018) — 139 citations
• Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (2017) — 147 citations