A. Shrestha

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 86 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #16,378 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #10,419 of 21,575 by trust here, #15,162 by citations
• Model Architectures — 12 citations into it
• Optimization & Training — 8 citations into it
• Signal Processing — 8 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom A. Shrestha trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ruslan Salakhutdinov
6. James Martens
7. Kaiming He
8. J. Urgen Schmidhuber
9. Sepp Hochreiter
10. Alex Krizhevsky

Who trusts A. Shrestha overall

1. Kurniawati Azizah
2. Carlos Vidal
3. Mohammad Mustafa Taye
4. Mirna Adriani
5. Pawel Malysz
6. Thorsten Hoeser
7. Lazhar Khelifi
8. Wisnu Jatmiko
9. Mohamed Loey
10. Ava Vali

Papers from A. Shrestha's perspective

Ordered by how strongly the researchers A. Shrestha 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. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Review of Deep Learning Algorithms and Architectures (2019) — 1,518 citations