Yogesh Singh Rawat

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 129 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Optimization & Training — 8 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Natural Language Processing — 5 citations into it

Whom Yogesh Singh Rawat trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Thorsten Joachims
4. Kaiming He
5. Yoshua Bengio
6. Alex Graves
7. Diederik P. Kingma
8. Yann LeCun
9. Ilya Sutskever
10. Max Welling

Papers from Yogesh Singh Rawat's perspective

Ordered by how strongly the researchers Yogesh Singh Rawat 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. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning (2021) — 655 citations