Seong-Lyun Kim

Artificial Intelligence · 2005–2020

A researcher in the Science corpus: 13 papers; trusts 42 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 132 citations into it; #10,862 of 35,345 by trust here, #6,517 by citations
• Electrical and Electronic Engineering — 71 citations into it; #3,340 of 16,539 by trust here, #4,881 by citations
• Computer Networks and Communications — 52 citations into it; #2,943 of 9,886 by trust here, #1,370 by citations
• Federated & Distributed Learning — 45 citations into it; #147 of 319 by trust here, #111 by citations
• Optimization & Training — 33 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it
• Efficiency & Compression — 23 citations into it; #989 of 1,264 by trust here, #653 by citations
• Computational Mechanics — 17 citations into it

Whom Seong-Lyun Kim trusts overall

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

1. Jihong Park
2. Martin Haenggi
3. Tianyang Bai
4. Jeffrey G. Andrews
5. H. Brendan McMahan
6. Medhi Bennis
7. Geoffrey E. Hinton
8. Rie Johnson
9. Xinchen Zhang
10. Tong Zhang

Who trusts Seong-Lyun Kim overall

1. Jihong Park
2. Medhi Bennis
3. Sumudu Samarakoon
4. Hyesung Kim
5. Hyowoon Seo
6. Eunjeong Jeong
7. Anis Elgabli
8. Merouane Debbah
9. Seungeun Oh

Papers from Seong-Lyun Kim's perspective

Ordered by how strongly the researchers Seong-Lyun Kim 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. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Attention is All you Need (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data (2018) — 759 citations
• User-Centric Mobility Management in Ultra-Dense Cellular Networks under Spatio-Temporal Dynamics (2016) — 39 citations
• Joint subcarrier and power allocation in uplink OFDMA systems (2005) — 371 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
• Distilling On-Device Intelligence at the Network Edge (2019) — 33 citations
• Federated Reinforcement Distillation with Proxy Experience Memory (2019) — 19 citations
• Multi-hop Federated Private Data Augmentation with Sample Compression (2019) — 19 citations
• Blockchained On-Device Federated Learning (2018) — 737 citations
• On-Device Federated Learning via Blockchain and its Latency Analysis (2018) — 117 citations
• Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach (2018) — 39 citations
• Ultra-dense edge caching under spatio-temporal demand and network dynamics (2017) — 27 citations