K. Bryan

Artificial Intelligence · 2009–2010

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom K. Bryan trusts overall

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

  1. Julian D. Olden
  2. Judith E. Dayhoff

Who trusts K. Bryan overall

  1. A. Zaji

Papers from K. Bryan's perspective

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

  1. Learning representations by back-propagating errors
  2. Review and comparison of methods to study the contribution of variables in artificial neural network models
  3. Use of some sensitivity criteria for choosing networks with good generalization ability
  4. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks
  5. Model selection and multimodel inference : a practical information-theoretic approach
  6. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data
  7. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences
  8. Backpropagation is Sensitive to Initial Conditions
  9. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
  10. Time series forecasting with neural networks: a comparative study using the air line data

Their papers