Azalia Mirhoseini

Artificial Intelligence · 2017–2025

A researcher in the Science corpus: 11 papers; trusts 200 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 274 citations into it; #26,654 of 35,345 by trust here, #1,616 by citations
• Computer Vision and Pattern Recognition — 103 citations into it; #16,448 of 21,575 by trust here, #2,165 by citations
• Natural Language Processing — 66 citations into it
• Model Architectures — 49 citations into it; #1,755 of 2,278 by trust here, #304 by citations
• Optimization & Training — 31 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Reinforcement Learning — 23 citations into it
• Signal Processing — 15 citations into it

Whom Azalia Mirhoseini trusts overall

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

1. Ilya Sutskever
2. Ronald J. Williams
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yoshua Bengio
7. Oriol Vinyals
8. Christian Szegedy
9. Sepp Hochreiter
10. Kaiming He

Who trusts Azalia Mirhoseini overall

1. Richard Ngo
2. Matei A. Zaharia
3. Noam Shazeer
4. David R Ha
5. Barret Zoph
6. Yi Tay
7. Joan Puigcerver
8. Minjie Wang
9. Carlos Riquelme
10. Yoshua Bengio

Papers from Azalia Mirhoseini's perspective

Ordered by how strongly the researchers Azalia Mirhoseini 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. Going deeper with convolutions

Their papers

• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (2017) — 5,113 citations
• Constitutional AI: Harmlessness from AI Feedback (2022) — 3,377 citations
• Device Placement Optimization with Reinforcement Learning (2017) — 492 citations
• The Capacity for Moral Self-Correction in Large Language Models (2023) — 214 citations
• Measuring Progress on Scalable Oversight for Large Language Models (2022) — 226 citations
• A graph placement methodology for fast chip design (2021) — 775 citations
• Representing Long-Range Context for Graph Neural Networks with Global Attention (2022) — 423 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Large Language Monkeys: Scaling Inference Compute with Repeated Sampling (2024) — 866 citations
• FLAME: Taming Backdoors in Federated Learning (2022) — 494 citations
• Deep Mixture of Experts via Shallow Embedding (2018) — 154 citations