Renato Negrinho

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 122 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #4,102 of 35,345 by trust here, #10,942 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Optimization & Training — 28 citations into it
• Management Science and Operations Research — 14 citations into it
• Model Architectures — 14 citations into it
• Neural Architecture Search & AutoML — 13 citations into it; #27 of 283 by trust here, #98 by citations
• Natural Language Processing — 12 citations into it
• Representation & Self-Supervised Learning — 12 citations into it

Whom Renato Negrinho trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. James Bergstra
4. Ilya Sutskever
5. Yoshua Bengio
6. Oriol Vinyals
7. Alex Graves
8. L. Eon Bottou
9. Frank Hutter
10. Kaiming He

Who trusts Renato Negrinho overall

1. Martin Wistuba
2. Barret Zoph
3. Ramakanth Pasunuru
4. Tal Ben-Nun
5. Marius Thomas Lindauer
6. Danilo Vasconcellos Vargas
7. Xiangxiang Chu
8. Purushotham Kamath
9. Linnan Wang
10. Xiaoliang Dai

Papers from Renato Negrinho's perspective

Ordered by how strongly the researchers Renato Negrinho trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• DeepArchitect: Automatically Designing and Training Deep Architectures (2017) — 194 citations
• Learning Beam Search Policies via Imitation Learning (2018) — 28 citations
• Towards modular and programmable architecture search (2019) — 31 citations
• Efficient Multitask Feature and Relationship Learning (2017) — 26 citations