Julian Salazar

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 528 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 136 citations into it; #10,997 of 35,345 by trust here, #9,760 by citations
• Natural Language Processing — 89 citations into it; #2,957 of 5,688 by trust here, #3,014 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Signal Processing — 41 citations into it; #3,326 of 7,572 by trust here, #2,743 by citations
• Model Architectures — 34 citations into it; #618 of 2,278 by trust here, #1,191 by citations
• Speech & Audio — 32 citations into it; #415 of 1,076 by trust here, #528 by citations
• Optimization & Training — 23 citations into it
• Representation & Self-Supervised Learning — 17 citations into it; #1,384 of 3,145 by trust here, #1,982 by citations

Whom Julian Salazar trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Alex Graves
5. Diederik P. Kingma
6. Ilya Sutskever
7. Kaiming He
8. Yann LeCun
9. Oriol Vinyals
10. Tomas Mikolov

Who trusts Julian Salazar overall

1. Timo Schick
2. Qiang Wang
3. Abdel-rahman Mohamed
4. Tu Nguyen
5. Toan Q. Nguyen
6. Chiara Plizzari
7. Gianni Brauwers
8. Shuhao Cao
9. Karolina Stańczak
10. Hinrich Schütze

Papers from Julian Salazar's perspective

Ordered by how strongly the researchers Julian Salazar 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Masked Language Model Scoring (2019) — 615 citations
• Deep Contextualized Acoustic Representations for Semi-Supervised Speech Recognition (2019) — 150 citations
• Self-attention Networks for Connectionist Temporal Classification in Speech Recognition (2019) — 125 citations
• Transformers without Tears: Improving the Normalization of Self-Attention (2019) — 278 citations
• Invariant representation learning for robust deep networks (2018) — 4 citations