Yi Zhou

Artificial Intelligence · with Anwar · 2019–2023

A researcher in the Science corpus: 6 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it
• Federated & Distributed Learning — 35 citations into it
• Privacy & Security — 26 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Sociology and Political Science — 8 citations into it

Whom Yi Zhou trusts overall

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

1. Ivan Bjerre Damgård
2. Cynthia Dwork
3. Stacey Truex
4. Reza Shokri
5. Milad Nasr
6. H. Brendan McMahan
7. Dan Boneh
8. Mads Jurik
9. Martín Abadi

Who trusts Yi Zhou overall

1. Li Li

Papers from Yi Zhou's perspective

Ordered by how strongly the researchers Yi Zhou 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. Gradient-based learning applied to document recognition
4. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Language Models are Few-Shot Learners (Neural Information Processing Systems)

Their papers

• mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality (2023) — 1,270 citations
• IBM Federated Learning: an Enterprise Framework White Paper V0.1 (2020) — 198 citations
• TiFL: A Tier-based Federated Learning System (2020) — 370 citations
• HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning (2019) — 321 citations
• Towards Federated Graph Learning for Collaborative Financial Crimes Detection (2019) — 82 citations
• Towards Taming the Resource and Data Heterogeneity in Federated Learning (2019) — 94 citations