Swaroop Indra Ramaswamy

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 5 papers; trusts 17 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #19,589 of 35,345 by trust here, #14,438 by citations
• Privacy & Security — 43 citations into it
• Optimization & Training — 23 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Federated & Distributed Learning — 15 citations into it
• Natural Language Processing — 12 citations into it; #4,280 of 5,688 by trust here, #3,574 by citations
• Sociology and Political Science — 9 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Swaroop Indra Ramaswamy trusts overall

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

1. H. Brendan McMahan
2. Ilya Sutskever
3. Cynthia Dwork
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Yoshua Bengio
7. Alex Krizhevsky
8. J. Urgen Schmidhuber
9. Kyunghyun Cho
10. Yann LeCun

Who trusts Swaroop Indra Ramaswamy overall

1. Li Li
2. Nicholas Carlini
3. Peter Kairouz
4. Florian Tramèr
5. Yuxi Fan
6. Daphne Ippolito
7. Laura Weidinger
8. Xuechen Li
9. Antonio A. Ginart
10. Lixu Wang

Papers from Swaroop Indra Ramaswamy's perspective

Ordered by how strongly the researchers Swaroop Indra Ramaswamy 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Training Production Language Models without Memorizing User Data (2020) — 97 citations
• Understanding Unintended Memorization in Federated Learning (2020) — 49 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
• Federated Learning for Emoji Prediction in a Mobile Keyboard (2019) — 334 citations