Adam Gaier

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 152 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Model Architectures — 16 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Reinforcement Learning — 11 citations into it
• Cognitive Neuroscience — 9 citations into it
• Neural Architecture Search & AutoML — 8 citations into it; #151 of 283 by trust here, #191 by citations

Whom Adam Gaier trusts overall

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

1. Kenneth O. Stanley
2. Jorma Rissanen
3. Geoffrey E. Hinton
4. David R Ha
5. Yann LeCun
6. J. Urgen Schmidhuber
7. Ronald J. Williams
8. Alex Graves
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Adam Gaier overall

1. Grace W. Lindsay
2. C. Freeman
3. Luke Metz
4. David R Ha
5. Katherine L. Hermann
6. Yujin Tang
7. Blake Camp
8. R'obert Csord'as
9. Pei Wang

Papers from Adam Gaier's perspective

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

Their papers

• Weight Agnostic Neural Networks (2019) — 262 citations
• Data-efficient neuroevolution with kernel-based surrogate models (2018) — 20 citations