Vijay Prakash Dwivedi

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 5 papers; trusts 196 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 172 citations into it; #5,303 of 35,345 by trust here, #10,566 by citations
• Computer Vision and Pattern Recognition — 83 citations into it
• Graph Learning — 78 citations into it
• Materials Chemistry — 53 citations into it; #820 of 11,513 by trust here, #8,635 by citations
• Model Architectures — 42 citations into it
• Computational Theory and Mathematics — 36 citations into it
• Molecular Biology — 29 citations into it
• Statistical and Nonlinear Physics — 29 citations into it

Whom Vijay Prakash Dwivedi trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Xavier Bresson
5. Yann LeCun
6. Alex Krizhevsky
7. Kaiming He
8. Sergey Ioffe
9. Thomas Kipf
10. Max Welling

Who trusts Vijay Prakash Dwivedi overall

1. Dexiong Chen
2. Chengxuan Ying
3. Ladislav Rampášek
4. Shaked Brody
5. Devin Kreuzer
6. Anh Tuan Luu
7. Mikhail Galkin
8. Leslie O’Bray
9. Tianle Cai
10. Dominique Beaini

Papers from Vijay Prakash Dwivedi's perspective

Ordered by how strongly the researchers Vijay Prakash Dwivedi 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Benchmarking Graph Neural Networks (2023) — 1,223 citations
• A Generalization of Transformer Networks to Graphs (2020) — 1,129 citations
• Graph Neural Networks with Learnable Structural and Positional Representations (2021) — 497 citations
• Long Range Graph Benchmark (2022) — 345 citations
• Recipe for a General, Powerful, Scalable Graph Transformer (2022) — 1,075 citations