Jihong Park

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 28 papers; trusts 176 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 306 citations into it; #6,350 of 35,345 by trust here, #4,316 by citations
• Computer Networks and Communications — 183 citations into it; #1,129 of 9,886 by trust here, #426 by citations
• Electrical and Electronic Engineering — 181 citations into it; #1,630 of 16,539 by trust here, #3,112 by citations
• Optimization & Training — 108 citations into it; #2,335 of 2,601 by trust here, #2,162 by citations
• Federated & Distributed Learning — 87 citations into it; #139 of 319 by trust here, #99 by citations
• Computer Vision and Pattern Recognition — 83 citations into it
• Computational Mechanics — 64 citations into it; #6,462 of 10,001 by trust here, #5,567 by citations
• Efficiency & Compression — 51 citations into it; #1,155 of 1,264 by trust here, #653 by citations

Whom Jihong Park trusts overall

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

1. Medhi Bennis
2. Geoffrey E. Hinton
3. Martin Haenggi
4. Hyesung Kim
5. H. Brendan McMahan
6. Alex Krizhevsky
7. Yoshua Bengio
8. Anis Elgabli
9. Yann LeCun
10. Diederik P. Kingma

Who trusts Jihong Park overall

1. Medhi Bennis
2. Anis Elgabli
3. Sumudu Samarakoon
4. Chaouki Ben Issaid
5. Mingzhe Chen
6. Hyesung Kim
7. Merouane Debbah
8. Khaled Ben Letaief
9. Eunjeong Jeong
10. Hyowoon Seo

Papers from Jihong Park's perspective

Ordered by how strongly the researchers Jihong Park 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. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
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

• Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction (2020) — 72 citations
• Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks (2018) — 362 citations
• Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data (2018) — 759 citations
• One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction (2019) — 20 citations
• Wireless Network Intelligence at the Edge (2018) — 588 citations
• URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems (2018) — 54 citations
• Ultra-Reliable Low-Latency Communication (URLLC): Principles and Building Blocks (2017) — 55 citations
• GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning (2019) — 91 citations
• Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning (2019) — 74 citations
• Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning (2020) — 72 citations
• User-Centric Mobility Management in Ultra-Dense Cellular Networks under Spatio-Temporal Dynamics (2016) — 39 citations
• Tractable Resource Management With Uplink Decoupled Millimeter-Wave Overlay in Ultra-Dense Cellular Networks (2015) — 107 citations
• Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020) — 185 citations
• Federated Knowledge Distillation (2020) — 109 citations
• Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM (2020) — 13 citations
• Extreme URLLC: Vision, Challenges, and Key Enablers (2020) — 109 citations
• Communication and Consensus Co-Design for Low-Latency and Reliable Industrial IoT Systems (2019) — 3 citations
• Distilling On-Device Intelligence at the Network Edge (2019) — 33 citations
• Federated Reinforcement Distillation with Proxy Experience Memory (2019) — 19 citations
• Wireless Edge Computing With Latency and Reliability Guarantees (2019) — 149 citations