Michael S. Gashler

Artificial Intelligence · 2017

A researcher in the Science corpus: 2 papers; trusts 244 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #24,922 of 35,345 by trust here, #34,213 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #18,193 of 21,575 by trust here, #19,310 by citations
• Model Architectures — 20 citations into it
• Optimization & Training — 17 citations into it
• Reinforcement Learning — 17 citations into it
• Control and Systems Engineering — 16 citations into it; #6,905 of 8,257 by trust here, #6,417 by citations
• Biomedical Engineering — 9 citations into it
• Human-Computer Interaction — 6 citations into it

Whom Michael S. Gashler trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ilya Sutskever
7. Sergey Levine
8. Kaiming He
9. J. Urgen Schmidhuber
10. L. Eon Bottou

Who trusts Michael S. Gashler overall

1. Mikhail Iu. Leontev
2. Oludare Isaac Abiodun
3. Justin A. Sirignano

Papers from Michael S. Gashler's perspective

Ordered by how strongly the researchers Michael S. Gashler 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep learning in robotics: a review of recent research (2017) — 299 citations
• An Investigation of How Neural Networks Learn from the Experiences of Peers Through Periodic Weight Averaging (2017) — 16 citations