William W. Lytton

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom William W. Lytton trusts overall

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

  1. Maizar Raissi

Papers from William W. Lytton's perspective

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

  1. Automatic differentiation in machine learning: a survey
  2. Neural Ordinary Differential Equations
  3. Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
  4. Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
  5. Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
  6. Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations
  7. Inferring solutions of differential equations using noisy multi-fidelity data
  8. Intriguing properties of neural networks
  9. Machine learning of linear differential equations using Gaussian processes
  10. Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models

Their papers