Seungeun Oh
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 4 papers; trusts 6 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 42 citations into it; #18,868 of 35,345 by trust here, #9,760 by citations
- Computer Vision and Pattern Recognition — 14 citations into it
- Electrical and Electronic Engineering — 12 citations into it; #11,098 of 16,539 by trust here, #5,871 by citations
- Computer Networks and Communications — 8 citations into it
Whom Seungeun Oh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jihong Park
- Geoffrey E. Hinton
- H. Brendan McMahan
- Mehdi Mirza
- Eunjeong Jeong
- Medhi Bennis
- Simon Osindero
- Seong-Lyun Kim
- Robin C. Geyer
- Sumudu Samarakoon
Who trusts Seungeun Oh overall
- Jihong Park
- Anis Elgabli
- Zhuangdi Zhu
- Hong-You Chen
- Medhi Bennis
- Sumudu Samarakoon
- Jinhyun Ahn
- Chaoyang He
Papers from Seungeun Oh's perspective
Ordered by how strongly the researchers Seungeun Oh trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Asynchronous Methods for Deep Reinforcement Learning
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Attention is All you Need (Neural Information Processing Systems)
Their papers
- Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data (2018) — 759 citations
- Federated Knowledge Distillation (2020) — 109 citations
- Distilling On-Device Intelligence at the Network Edge (2019) — 33 citations
- Multi-hop Federated Private Data Augmentation with Sample Compression (2019) — 19 citations