Amine Elhattami

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 104 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #24,657 of 35,345 by trust here, #30,347 by citations
• Natural Language Processing — 43 citations into it; #4,117 of 5,688 by trust here, #5,214 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #15,261 of 21,575 by trust here, #19,310 by citations
• Evaluation & Benchmarks — 17 citations into it
• Large Language Models — 10 citations into it
• Optimization & Training — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it; #2,383 of 3,145 by trust here, #2,957 by citations

Whom Amine Elhattami trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Jimmy Ba
5. Alex Krizhevsky
6. Ilya Sutskever
7. Yann LeCun
8. R. Caruana
9. Ronan Collobert
10. Kaiming He

Who trusts Amine Elhattami overall

1. Joe Stacey
2. Tianyang Lin
3. Yonatan Belinkov

Papers from Amine Elhattami's perspective

Ordered by how strongly the researchers Amine Elhattami 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data (2020) — 105 citations