Justin Hong
Artificial Intelligence · 2019
A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 23 citations into it; #23,796 of 35,345 by trust here, #19,439 by citations
- Optimization & Training — 12 citations into it
- Statistics and Probability — 9 citations into it
- Computational Mechanics — 5 citations into it
- Computer Networks and Communications — 5 citations into it; #6,277 of 9,886 by trust here, #4,277 by citations
Whom Justin Hong trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Martin A. Zinkevich
- Dong Yin
- Stanislav Minsker
- Moses Charikar
- Yudong Chen
- Jacob Steinhardt
- Ilias Diakonikolas
- Robert A. Jacobs
- Geoffrey E. Hinton
- Michael I. Jordan
Who trusts Justin Hong overall
Papers from Justin Hong's perspective
Ordered by how strongly the researchers Justin Hong 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
- Learning Multiple Layers of Features from Tiny Images
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Robust Federated Learning in a Heterogeneous Environment (2019) — 262 citations