William R. Cannon

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom William R. Cannon trusts overall

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

  1. Maizar Raissi
  2. Paris Perdikaris
  3. Daniel T. Gillespie
  4. G. Em Karniadakis
  5. F. Sahli Costabal
  6. Michael D. Schmidt
  7. Karoline Faust
  8. Niels Klitgord
  9. Radhakrishnan Mahadevan
  10. Bill Shipley

Papers from William R. Cannon's perspective

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

  1. Intriguing properties of neural networks
  2. Automatic differentiation in machine learning: a survey
  3. Neural Ordinary Differential Equations
  4. Distilling Free-Form Natural Laws from Experimental Data
  5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  6. Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
  7. Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
  8. Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
  9. Deep Learning
  10. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Their papers