Sam Daulton
Artificial Intelligence · 2019–2020
A researcher in the Science corpus: 2 papers; trusts 30 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 51 citations into it
- Computational Theory and Mathematics — 29 citations into it
- Management Science and Operations Research — 19 citations into it
- Computer Vision and Pattern Recognition — 16 citations into it
- Probabilistic & Bayesian Methods — 16 citations into it
- Optimization & Training — 12 citations into it
- Neural Architecture Search & AutoML — 5 citations into it
Whom Sam Daulton trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald R. Jones
- Joshua D. Knowles
- Nikolaus Hansen
- Jian Wu
- Peter I. Frazier
- Kalyanmoy Deb
- Ryoji Tanabe
- James T. Wilson
- Benjamin Letham
- Diederik P. Kingma
Papers from Sam Daulton's perspective
Ordered by how strongly the researchers Sam Daulton trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Auto-Encoding Variational Bayes (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization (2020) — 381 citations
- BoTorch: Programmable Bayesian Optimization in PyTorch (2019) — 102 citations