Zied Bouraoui

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 103 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #13,647 of 35,345 by trust here, #18,586 by citations
• Computer Vision and Pattern Recognition — 39 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Natural Language Processing — 27 citations into it; #2,552 of 5,688 by trust here, #3,754 by citations
• Large Language Models — 8 citations into it; #278 of 799 by trust here, #644 by citations
• Model Architectures — 7 citations into it

Whom Zied Bouraoui trusts overall

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

1. Geoffrey E. Hinton
2. Tomas Mikolov
3. Yoav Goldberg
4. Yoshua Bengio
5. Kaiming He
6. Ilya Sutskever
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Jacob Devlin
10. Christopher D. Manning

Who trusts Zied Bouraoui overall

1. Timo Schick
2. Tara Safavi
3. Vered Shwartz
4. Hinrich Schütze
5. Anna Rogers
6. Bill Yuchen Lin
7. Tianxiang Sun
8. Abhilasha Ravichander
9. Eric P. Lehman
10. Wangchunshu Zhou

Papers from Zied Bouraoui's perspective

Ordered by how strongly the researchers Zied Bouraoui 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Inducing Relational Knowledge from BERT (2019) — 184 citations
• Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning (2021) — 36 citations