Richard Lee Davis

Probabilistic & Bayesian Methods · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 30 citations into it
• Artificial Intelligence — 24 citations into it
• Generative Models — 17 citations into it
• Statistics and Probability — 12 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Richard Lee Davis trusts overall

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

1. Danilo Jimenez Rezende
2. Michael I. Jordan
3. Radford M. Neal
4. R. Chalmers
5. Diederik P. Kingma
6. Ronald J. Williams
7. Martin J. Wainwright
8. Shakir Mohamed
9. Geoffrey E. Hinton
10. David M. Blei

Papers from Richard Lee Davis's perspective

Ordered by how strongly the researchers Richard Lee Davis 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Variational Item Response Theory: Fast, Accurate, and Expressive (2020) — 62 citations
• Modeling Item Response Theory with Stochastic Variational Inference (2021) — 5 citations