Amanda Rios

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 45 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #15,140 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #9,746 of 21,575 by trust here, #13,815 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Meta & Continual Learning — 9 citations into it; #470 of 1,041 by trust here, #756 by citations
• Evaluation & Benchmarks — 6 citations into it
• Generative Models — 6 citations into it; #913 of 1,956 by trust here, #1,485 by citations
• Model Architectures — 5 citations into it

Whom Amanda Rios trusts overall

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

1. R. M. French
2. Sylvestre-Alvise Rebuffi
3. Tommaso Furlanello
4. David Lopez-Paz
5. Sebastian Farquhar
6. Geoffrey E. Hinton
7. Augustus Odena
8. Marc'Aurelio Ranzato
9. Diederik P. Kingma
10. Alex Krizhevsky

Who trusts Amanda Rios overall

1. Mehrdad Farajtabar
2. Timothée Lesort
3. Seyed Iman Mirzadeh
4. Zichen Miao
5. Yulai Cong
6. Ze Wang
7. Miaoyun Zhao
8. Dong Yin
9. Vincenzo Lomonaco
10. Wei Chen

Papers from Amanda Rios's perspective

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

Their papers

• Closed-Loop GAN for continual Learning (2018) — 21 citations
• Closed-Loop Memory GAN for Continual Learning (2018) — 64 citations