Maruan Al-Shedivat

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 11 papers; trusts 508 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 431 citations into it; #9,254 of 35,345 by trust here, #3,673 by citations
• Computer Vision and Pattern Recognition — 122 citations into it; #14,945 of 21,575 by trust here, #13,269 by citations
• Optimization & Training — 110 citations into it
• Natural Language Processing — 97 citations into it; #4,029 of 5,688 by trust here, #4,601 by citations
• Federated & Distributed Learning — 75 citations into it
• Reinforcement Learning — 71 citations into it; #1,248 of 2,242 by trust here, #510 by citations
• Safety, Robustness & Interpretability — 51 citations into it
• Probabilistic & Bayesian Methods — 34 citations into it

Whom Maruan Al-Shedivat trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ilya Sutskever
6. Yann LeCun
7. Ronald J. Williams
8. J. Urgen Schmidhuber
9. Richard S. Sutton
10. Alex Graves

Who trusts Maruan Al-Shedivat overall

1. Georgios Papoudakis
2. Dong-Ki Kim
3. Chelsea Finn
4. Yash Chandak
5. Matthew Riemer
6. Jakob N. Foerster
7. Sergey Levine
8. Xavier García
9. Ferran Alet
10. Trapit Bansal

Papers from Maruan Al-Shedivat's perspective

Ordered by how strongly the researchers Maruan Al-Shedivat 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments (2017) — 376 citations
• Learning with Opponent-Learning Awareness (2017) — 622 citations
• Contextual Explanation Networks (2017) — 90 citations
• Learning Scalable Deep Kernels with Recurrent Structure (2016) — 116 citations
• Learning Policy Representations in Multiagent Systems (2018) — 137 citations
• Consistency by Agreement in Zero-Shot Neural Machine Translation (2019) — 57 citations
• A Field Guide to Federated Optimization (2021) — 487 citations
• Progressive Generation of Long Text with Pretrained Language Models (2021) — 107 citations
• Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms (2020) — 129 citations
• Regularizing Black-box Models for Improved Interpretability (2019) — 85 citations
• DiCE: The Infinitely Differentiable Monte-Carlo Estimator (2018) — 107 citations