G. Slabaugh

Artificial Intelligence · 2004–2020

A researcher in the Science corpus: 8 papers; trusts 301 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 180 citations into it; #15,786 of 35,345 by trust here, #3,652 by citations
• Computer Vision and Pattern Recognition — 161 citations into it; #10,138 of 21,575 by trust here, #2,768 by citations
• Meta & Continual Learning — 77 citations into it; #276 of 1,041 by trust here, #28 by citations
• Representation & Self-Supervised Learning — 60 citations into it
• Optimization & Training — 47 citations into it
• Reinforcement Learning — 24 citations into it
• Model Architectures — 21 citations into it
• Computational Mechanics — 18 citations into it

Whom G. Slabaugh trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Yann LeCun
6. Ilya Sutskever
7. Diederik P. Kingma
8. Karen Simonyan
9. Rahaf Aljundi
10. Christian Szegedy

Who trusts G. Slabaugh overall

1. Gyuhak Kim
2. Zixuan Ke
3. S. M. Seitz
4. Marc Masana
5. Yaoyao Liu

Papers from G. Slabaugh's perspective

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

Their papers

• A Continual Learning Survey: Defying Forgetting in Classification Tasks (2019) — 2,435 citations
• Methods for Volumetric Reconstruction of Visual Scenes (2004) — 138 citations
• More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning (2020) — 115 citations
• Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem (2020) — 26 citations
• Continual learning: A comparative study on how to defy forgetting in classification tasks (2019) — 260 citations
• DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018) — 985 citations
• Accuracy and Interpretability Trade-Offs in Machine Learning Applied to Safer Gambling (2016) — 30 citations
• Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images (2009) — 316 citations