Alan R. Wagner

Artificial Intelligence · 2012–2021

A researcher in the Science corpus: 12 papers; trusts 1,259 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 158 citations into it; #6,147 of 35,345 by trust here, #11,921 by citations
• Computer Vision and Pattern Recognition — 143 citations into it; #5,776 of 21,575 by trust here, #10,010 by citations
• Meta & Continual Learning — 71 citations into it; #227 of 1,041 by trust here, #660 by citations
• Representation & Self-Supervised Learning — 42 citations into it; #2,047 of 3,145 by trust here, #2,691 by citations
• Cognitive Neuroscience — 25 citations into it; #8,143 of 10,852 by trust here, #10,026 by citations
• Evaluation & Benchmarks — 25 citations into it
• Model Architectures — 21 citations into it
• Optimization & Training — 20 citations into it

Whom Alan R. Wagner trusts overall

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

1. Alex Krizhevsky
2. Ali Ayub
3. Kaiming He
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Yann LeCun
7. David G. Lowe
8. Ross B. Girshick
9. Andrew Zisserman
10. Yoshua Bengio

Who trusts Alan R. Wagner overall

1. Ali Ayub
2. C. Fendley
3. Tyler L. Hayes
4. Christopher Kanan
5. Liyuan Wang
6. Chrystopher L. Nehaniv
7. Anna Kukleva
8. Tero Karppi
9. Gyuhak Kim
10. Haoxuan Qu

Papers from Alan R. Wagner's perspective

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

Their papers

• Cognitively-Inspired Model for Incremental Learning Using a Few Examples (2020) — 51 citations
• Overtrust of robots in emergency evacuation scenarios (2016) — 424 citations
• Learning Novel Objects Continually Through Curiosity (2021) — 4 citations
• EEC: Learning to Encode and Regenerate Images for Continual Learning (2021) — 63 citations
• F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning (2021) — 14 citations
• Online Learning of Objects through Curiosity-Driven Active Learning (2021) — 3 citations
• Brain-Inspired Model for Incremental Learning Using a Few Examples (2020) — 3 citations
• What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper (2020) — 14 citations
• Tell me what this is: Few-Shot Incremental Object Learning by a Robot (2020) — 34 citations
• Storing Encoded Episodes as Concepts for Continual Learning (2020) — 13 citations
• Centroid Based Concept Learning for RGB-D Indoor Scene Classification (2019) — 32 citations
• Moral Decision Making in Autonomous Systems: Enforcement, Moral Emotions, Dignity, Trust, and Deception (2012) — 170 citations