Thomas Y. Hou

Artificial Intelligence · with Halverson · 2024

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Thomas Y. Hou trusts

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

  1. Johannes Schmidt-Hieber (in Artificial Intelligence)

Papers from Thomas Y. Hou's perspective

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

  1. Attention is All you Need (Neural Information Processing Systems)
  2. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators (Nature Machine Intelligence)
  3. Fourier Neural Operator for Parametric Partial Differential Equations (International Conference on Learning Representations)
  4. Implicit Neural Representations with Periodic Activation Functions
  5. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
  6. Physics-Informed Neural Operator for Learning Partial Differential Equations (ACM / IMS Journal of Data Science)
  7. Deep Learning Scaling is Predictable, Empirically
  8. Why Does Deep and Cheap Learning Work So Well?
  9. Nonparametric regression using deep neural networks with ReLU activation function
  10. Deep Sets

Their papers