Vikram Nitin

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 47 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #32,897 of 35,345 by trust here, #19,018 by citations
• Graph Learning — 43 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Knowledge Graphs & Representation — 10 citations into it
• Management Science and Operations Research — 10 citations into it
• Molecular Biology — 10 citations into it
• Natural Language Processing — 10 citations into it

Whom Vikram Nitin trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Antoine Bordes
4. Diederik P. Kingma
5. Richard Socher
6. Jason Aaron Edward Weston
7. Alex Krizhevsky
8. Thomas Kipf
9. Ilya Sutskever
10. Maximilian Nickel

Who trusts Vikram Nitin overall

1. Jing Zhang

Papers from Vikram Nitin's perspective

Ordered by how strongly the researchers Vikram Nitin 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Composition-based Multi-Relational Graph Convolutional Networks (2019) — 1,165 citations
• InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions (2019) — 399 citations
• HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs (2018) — 681 citations