Junshan Zhang

Artificial Intelligence · 2005–2022

A researcher in the Science corpus: 12 papers; trusts 356 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 213 citations into it; #23,130 of 35,345 by trust here, #10,667 by citations
• Computer Vision and Pattern Recognition — 124 citations into it
• Computer Networks and Communications — 69 citations into it; #4,279 of 9,886 by trust here, #1,714 by citations
• Electrical and Electronic Engineering — 54 citations into it; #5,515 of 16,539 by trust here, #8,489 by citations
• Representation & Self-Supervised Learning — 44 citations into it
• Meta & Continual Learning — 33 citations into it
• Federated & Distributed Learning — 31 citations into it
• Optimization & Training — 31 citations into it

Whom Junshan Zhang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Marc'Aurelio Ranzato
6. Kaiming He
7. Diederik P. Kingma
8. Ilya Sutskever
9. Karen Simonyan
10. Arslan Chaudhry

Who trusts Junshan Zhang overall

1. Anders Høst-Madsen
2. Alexander A. Alemi
3. Yindi Jing
4. Boris Rankov

Papers from Junshan Zhang's perspective

Ordered by how strongly the researchers Junshan Zhang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Going deeper with convolutions

Their papers

• A Collaborative Learning Framework via Federated Meta-Learning (2020) — 95 citations
• On the Relation Between Identifiability, Differential Privacy, and Mutual-Information Privacy (2016) — 28 citations
• Capacity bounds and power allocation for wireless relay channels (2005) — 1,161 citations
• A Spatio-Temporal Analysis Approach for Short-Term Forecast of Wind Farm Generation (2014) — 132 citations
• CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices (2020) — 311 citations
• Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer (2022) — 81 citations
• TRGP: Trust Region Gradient Projection for Continual Learning (2022) — 116 citations
• GROWN: GRow Only When Necessary for Continual Learning (2021) — 15 citations
• FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring (2020) — 391 citations
• Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory (2019) — 793 citations
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing (2019) — 1,896 citations
• On the relation between identifiability, differential privacy, and mutual-information privacy (2014) — 179 citations