Zachary Nado

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 9 papers; trusts 205 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 351 citations into it; #31,491 of 35,345 by trust here, #2,863 by citations
• Computer Vision and Pattern Recognition — 147 citations into it
• Optimization & Training — 126 citations into it
• Evaluation & Benchmarks — 87 citations into it
• Natural Language Processing — 85 citations into it
• Safety, Robustness & Interpretability — 79 citations into it
• Model Architectures — 36 citations into it
• Representation & Self-Supervised Learning — 25 citations into it

Whom Zachary Nado trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Sergey Ioffe
8. Christian Szegedy
9. Ilya Sutskever
10. Dan Hendrycks

Who trusts Zachary Nado overall

1. Marvin Zhang
2. Shuaicheng Niu
3. Dequan Wang
4. Sergey Levine
5. Lei Huang
6. Taesik Gong
7. Jiaxiang Wu
8. Chelsea Finn
9. Yifan Zhang

Papers from Zachary Nado's perspective

Ordered by how strongly the researchers Zachary Nado 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• PaLM 2 Technical Report (2023) — 1,524 citations
• Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift (2019) — 2,269 citations
• Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model (2019) — 192 citations
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift (2020) — 332 citations
• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Adaptive Gradient Methods at the Edge of Stability (2022) — 86 citations
• On Empirical Comparisons of Optimizers for Deep Learning (2019) — 304 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations