Michael A. Chappell

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 7 citations into it
• Radiology, Nuclear Medicine and Imaging — 7 citations into it
• Cognitive Neuroscience — 6 citations into it
• Statistics and Probability — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Michael A. Chappell trusts overall

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

1. Diederik P. Kingma
2. Hagai Thomas Attias
3. Matthew J. Beal
4. Matthew D. Hoffman
5. Stephen M. Smith
6. Max Welling
7. Jimmy Ba
8. Mark Jenkinson
9. David M. Blei
10. Geoffrey E. Hinton

Papers from Michael A. Chappell's perspective

Ordered by how strongly the researchers Michael A. Chappell trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Stochastic Variational Bayesian Inference for a Nonlinear Forward Model (2020) — 391 citations
• Bayesian analysis of neuroimaging data in FSL (2009) — 2,686 citations