Rajiv Mathews

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 10 papers; trusts 80 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 191 citations into it; #10,767 of 35,345 by trust here, #4,137 by citations
• Privacy & Security — 58 citations into it
• Optimization & Training — 43 citations into it; #2,025 of 2,601 by trust here, #606 by citations
• Computer Vision and Pattern Recognition — 37 citations into it
• Federated & Distributed Learning — 34 citations into it
• Natural Language Processing — 32 citations into it; #3,534 of 5,688 by trust here, #2,422 by citations
• Safety, Robustness & Interpretability — 32 citations into it; #1,294 of 2,251 by trust here, #1,650 by citations
• Signal Processing — 29 citations into it

Whom Rajiv Mathews trusts overall

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

1. H. Brendan McMahan
2. Yurii E. Nesterov
3. Ilya Sutskever
4. Sepp Hochreiter
5. J. Urgen Schmidhuber
6. Geoffrey E. Hinton
7. Diederik P. Kingma
8. Yoshua Bengio
9. Cynthia Dwork
10. Alex Krizhevsky

Who trusts Rajiv Mathews overall

1. Li Li
2. Mehryar Mohri
3. Fan Lai
4. Peter Kairouz
5. Andrew Straiton Hard
6. Yutao Huang
7. Yishay Mansour
8. Sai Praneeth Karimireddy
9. Nicholas Carlini
10. Xinle Liang

Papers from Rajiv Mathews's perspective

Ordered by how strongly the researchers Rajiv Mathews 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Training Production Language Models without Memorizing User Data (2020) — 97 citations
• Understanding Unintended Memorization in Federated Learning (2020) — 49 citations
• Federated Learning for Mobile Keyboard Prediction (2018) — 1,848 citations
• Federated Learning Of Out-Of-Vocabulary Words (2019) — 182 citations
• Federated Evaluation of On-device Personalization (2019) — 321 citations
• Generative Models for Effective ML on Private, Decentralized Datasets (2019) — 211 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Training Keyword Spotting Models on Non-IID Data with Federated Learning (2020) — 74 citations
• Federated Learning for Emoji Prediction in a Mobile Keyboard (2019) — 334 citations
• Federated Learning of N-Gram Language Models (2019) — 80 citations