Amjad Almahairi

Artificial Intelligence · 2015–2024

A researcher in the Science corpus: 11 papers; trusts 272 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 241 citations into it; #20,102 of 35,345 by trust here, #1,521 by citations
• Computer Vision and Pattern Recognition — 127 citations into it; #13,518 of 21,575 by trust here, #5,422 by citations
• Natural Language Processing — 73 citations into it; #5,376 of 5,688 by trust here, #4,539 by citations
• Large Language Models — 60 citations into it
• Generative Models — 43 citations into it; #1,388 of 1,956 by trust here, #1,134 by citations
• Optimization & Training — 41 citations into it
• Model Architectures — 32 citations into it
• Prompting & Reasoning — 28 citations into it

Whom Amjad Almahairi trusts overall

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

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

Who trusts Amjad Almahairi overall

1. Felix Hill
2. Yoshua Bengio
3. Thomas Verelst
4. Ji Lin
5. Trapit Bansal
6. Yongfeng Zhang
7. Lanlan Liu
8. Ali Ehteshami Bejnordi
9. Massimo Quadrana
10. Kyunghyun Cho

Papers from Amjad Almahairi's perspective

Ordered by how strongly the researchers Amjad Almahairi 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) — 17,670 citations
• Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data (2018) — 465 citations
• Calibrating Energy-based Generative Adversarial Networks (2017) — 111 citations
• Dynamic Capacity Networks (2015) — 109 citations
• Learning Distributed Representations from Reviews for Collaborative Filtering (2015) — 114 citations
• A Closer Look at the Optimization Landscapes of Generative Adversarial Networks (2019) — 66 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• RouteLLM: Learning to Route LLMs with Preference Data (2024) — 517 citations
• Progressive Prompts: Continual Learning for Language Models (2023) — 243 citations
• UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning (2021) — 235 citations
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations