Ronald Kemker

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 5 papers; trusts 456 researchers and is trusted by 214 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it; #1,361 of 35,345 by trust here, #2,734 by citations
• Computer Vision and Pattern Recognition — 80 citations into it; #4,934 of 21,575 by trust here, #3,929 by citations
• Cognitive Neuroscience — 63 citations into it; #142 of 10,852 by trust here, #948 by citations
• Neuroscience — 48 citations into it; #21 of 487 by trust here, #49 by citations
• Model Architectures — 43 citations into it
• Cellular and Molecular Neuroscience — 37 citations into it
• Meta & Continual Learning — 33 citations into it; #18 of 1,041 by trust here, #86 by citations
• Optimization & Training — 27 citations into it; #898 of 2,601 by trust here, #590 by citations

Whom Ronald Kemker trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. R. M. French
4. Alex Krizhevsky
5. Yoshua Bengio
6. Diederik P. Kingma
7. Yann LeCun
8. Ross B. Girshick
9. X. Zhang
10. Karen Simonyan

Who trusts Ronald Kemker overall

1. Tyler L. Hayes
2. Ali Ayub
3. Christopher Kanan
4. Arthur Douillard
5. Vincenzo Lomonaco
6. Eden Belouadah
7. Davide Maltoni
8. German Ignacio Parisi
9. Gido M. van de Ven
10. Hongjoon Ahn

Papers from Ronald Kemker's perspective

Ordered by how strongly the researchers Ronald Kemker 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Measuring Catastrophic Forgetting in Neural Networks (2017) — 889 citations
• FearNet: Brain-Inspired Model for Incremental Learning (2017) — 523 citations
• Continual Lifelong Learning with Neural Networks: A Review (2018) — 3,616 citations
• New Metrics and Experimental Paradigms for Continual Learning (2018) — 37 citations
• Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning (2017) — 527 citations