Jaekyun Moon

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 4 papers; trusts 222 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #17,271 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 53 citations into it; #10,916 of 21,575 by trust here, #11,950 by citations
• Meta & Continual Learning — 34 citations into it
• Optimization & Training — 25 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Computational Mechanics — 8 citations into it
• Model Architectures — 5 citations into it

Whom Jaekyun Moon trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Oriol Vinyals
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. Jimmy Ba
10. Ross B. Girshick

Who trusts Jaekyun Moon overall

1. Steinar Laenen

Papers from Jaekyun Moon's perspective

Ordered by how strongly the researchers Jaekyun Moon 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning (2020) — 49 citations
• TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning (2019) — 302 citations
• Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning (2023) — 75 citations
• Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks (2019) — 41 citations