Eduardo Valle

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 298 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #4,976 of 35,345 by trust here, #5,187 by citations
• Computer Vision and Pattern Recognition — 94 citations into it; #5,583 of 21,575 by trust here, #4,690 by citations
• Representation & Self-Supervised Learning — 38 citations into it
• Meta & Continual Learning — 27 citations into it; #217 of 1,041 by trust here, #153 by citations
• Radiology, Nuclear Medicine and Imaging — 17 citations into it; #348 of 3,265 by trust here, #486 by citations
• Optimization & Training — 15 citations into it
• Safety, Robustness & Interpretability — 15 citations into it
• Generative Models — 12 citations into it

Whom Eduardo Valle trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Kaiming He
6. Christian Szegedy
7. Diederik P. Kingma
8. Ian J. Goodfellow
9. Ilya Sutskever
10. David G. Lowe

Who trusts Eduardo Valle overall

1. Arthur Douillard
2. Jake Zhao
3. Yaoyao Liu
4. A. Serban
5. Tyler L. Hayes
6. Katja Auernhammer
7. Sara Sabour
8. Naveed Akhtar
9. Da-Wei Zhou
10. Alhussein Fawzi

Papers from Eduardo Valle's perspective

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

Their papers

• PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning (2020) — 909 citations
• Exploring the space of adversarial images (2015) — 200 citations
• Adversarial Images for Variational Autoencoders (2016) — 92 citations
• Insights from the Future for Continual Learning (2020) — 12 citations
• Small-Task Incremental Learning (2020) — 7 citations