Diogo Almeida

Artificial Intelligence · with Targ · 2016–2022

A researcher in the Science corpus: 5 papers; trusts 226 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 138 citations into it; #11,539 of 35,345 by trust here, #921 by citations
• Computer Vision and Pattern Recognition — 71 citations into it; #7,958 of 21,575 by trust here, #7,176 by citations
• Optimization & Training — 43 citations into it
• Model Architectures — 32 citations into it; #917 of 2,278 by trust here, #1,048 by citations
• Safety, Robustness & Interpretability — 31 citations into it
• Reinforcement Learning — 19 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Computational Mechanics — 7 citations into it

Whom Diogo Almeida trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. J. Urgen Schmidhuber
9. Ilya Loshchilov
10. Karen Simonyan

Who trusts Diogo Almeida overall

1. Yu Cheng
2. Gustav Larsson
3. Alexander Kolesnikov
4. Jeff Donahue
5. J'er'emy Scheurer
6. Taco Cohen
7. Shan Li
8. Barret Zoph
9. Wenhu Chen

Papers from Diogo Almeida's perspective

Ordered by how strongly the researchers Diogo Almeida 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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

• Training language models to follow instructions with human feedback (2022) — 22,660 citations
• Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units (2016) — 530 citations
• Resnet in Resnet: Generalizing Residual Architectures (2016) — 1,099 citations
• A Generalizable Approach to Learning Optimizers (2021) — 35 citations
• Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures (2016) — 14 citations