Andrew Straiton Hard

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 4 papers; trusts 9 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 208 citations into it; #21,305 of 35,345 by trust here, #6,614 by citations
• Optimization & Training — 79 citations into it; #1,941 of 2,601 by trust here, #680 by citations
• Safety, Robustness & Interpretability — 20 citations into it
• Natural Language Processing — 14 citations into it
• Sociology and Political Science — 11 citations into it
• Speech & Audio — 11 citations into it
• Management Science and Operations Research — 9 citations into it

Whom Andrew Straiton Hard trusts overall

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

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

Who trusts Andrew Straiton Hard overall

1. Ahmed Khaled
2. Zachary B. Charles
3. Boyi Liu
4. Mehryar Mohri
5. Rajiv Mathews
6. Mingqing Chen
7. Shishir G. Patil
8. Jakub Konecný
9. Ananda Theertha Suresh
10. Haokun Fang

Papers from Andrew Straiton Hard's perspective

Ordered by how strongly the researchers Andrew Straiton Hard 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Federated Learning for Mobile Keyboard Prediction (2018) — 1,848 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• A Field Guide to Federated Optimization (2021) — 487 citations
• Training Keyword Spotting Models on Non-IID Data with Federated Learning (2020) — 74 citations