Dzmitry Huba

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it
• Optimization & Training — 47 citations into it
• Federated & Distributed Learning — 37 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Electrical and Electronic Engineering — 11 citations into it

Whom Dzmitry Huba trusts overall

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

1. Pascal Paillier
2. H. Brendan McMahan
3. Alex Krizhevsky
4. Shafi Goldwasser
5. Cong Xie
6. Yoon Kim
7. Jakub Konecný
8. Keith Bonawitz
9. Andrew Straiton Hard
10. Chenhao Xu

Papers from Dzmitry Huba's perspective

Ordered by how strongly the researchers Dzmitry Huba trusts endorse each paper. Same corpus, a different order for every researcher.

1. Learning Multiple Layers of Features from Tiny Images
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Large Scale Distributed Deep Networks
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Towards Federated Learning at Scale: System Design (2019) — 3,255 citations
• Papaya: Practical, Private, and Scalable Federated Learning (2021) — 172 citations
• Federated Learning with Buffered Asynchronous Aggregation (2021) — 469 citations