Charles K. Fisher

Artificial Intelligence · 2007–2018

A researcher in the Science corpus: 3 papers; trusts 18 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Optimization & Training — 25 citations into it
• Molecular Biology — 23 citations into it
• Statistics and Probability — 22 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Statistical and Nonlinear Physics — 16 citations into it

Whom Charles K. Fisher trusts overall

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

1. Leo Breiman
2. Jonathan Friedman
3. Geoffrey E. Hinton
4. Elizabeth M. Glass
5. Radford M. Neal
6. Peter J. Turnbaugh
7. Eric J. Alm
8. J. Gregory Caporaso
9. Vanessa K. Ridaura
10. Diederik P. Kingma

Who trusts Charles K. Fisher overall

1. M. Lynch

Papers from Charles K. Fisher's perspective

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

Their papers

• Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression (2014) — 328 citations
• A high-bias, low-variance introduction to Machine Learning for physicists (2018) — 1,003 citations
• Visualizing spatially correlated dynamics that directs RNA conformational transitions (2007) — 243 citations