Zachary B. Charles

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 13 papers; trusts 449 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 783 citations into it; #6,032 of 35,345 by trust here, #2,091 by citations
• Optimization & Training — 309 citations into it; #2,204 of 2,601 by trust here, #912 by citations
• Federated & Distributed Learning — 238 citations into it; #80 of 319 by trust here, #21 by citations
• Privacy & Security — 234 citations into it
• Computer Vision and Pattern Recognition — 169 citations into it
• Safety, Robustness & Interpretability — 127 citations into it
• Computer Networks and Communications — 117 citations into it
• ML Systems & Hardware — 92 citations into it; #170 of 575 by trust here, #329 by citations

Whom Zachary B. Charles trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. H. Brendan McMahan
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. L. Eon Bottou
10. Tian Li

Who trusts Zachary B. Charles overall

1. Deepesh Data
2. Tian Li
3. Linqi Song
4. Suhas N. Diggavi
5. Jianyu Wang
6. Sai Praneeth Karimireddy
7. Chaoyang He
8. Dimitris Papailiopoulos
9. Cong Xie
10. Peter Kairouz

Papers from Zachary B. Charles's perspective

Ordered by how strongly the researchers Zachary B. Charles 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• Does Data Augmentation Lead to Positive Margin? (2019) — 38 citations
• On the Outsized Importance of Learning Rates in Local Update Methods (2020) — 60 citations
• Adaptive Federated Optimization (2020) — 2,039 citations
• ATOMO: Communication-efficient Learning via Atomic Sparsification (2018) — 392 citations
• Gradient Coding Using the Stochastic Block Model (2018) — 26 citations
• On Large-Cohort Training for Federated Learning (2021) — 126 citations
• A Field Guide to Federated Optimization (2021) — 487 citations
• DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation (2019) — 149 citations
• A Geometric Perspective on the Transferability of Adversarial Directions (2018) — 17 citations
• DRACO: Robust Distributed Training via Redundant Gradients (2018) — 23 citations
• DRACO: Byzantine-resilient Distributed Training via Redundant Gradients (2018) — 274 citations
• Approximate Gradient Coding via Sparse Random Graphs (2017) — 89 citations